{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "e883922abb30947f", "metadata": { "collapsed": false }, "outputs": [], "source": [ "!python -m pip install git+https://github.com/huggingface/transformers.git\n", "!python -m pip install pytorch-crf\n", "!python -m pip install --upgrade pip\n", "!python -m pip install -r requirements.txt\n", "!python -m pip install \\\n", " --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple \\\n", " \"cudf-cu12>=24.4.0a0,<=24.4\" \"dask-cudf-cu12>=24.4.0a0,<=24.4\" \\\n", " \"cuml-cu12>=24.4.0a0,<=24.4\" \"cugraph-cu12>=24.4.0a0,<=24.4\" \\\n", " \"cuspatial-cu12>=24.4.0a0,<=24.4\" \"cuproj-cu12>=24.4.0a0,<=24.4\" \\\n", " \"cuxfilter-cu12>=24.4.0a0,<=24.4\" \"cucim-cu12>=24.4.0a0,<=24.4\" \\\n", " \"pylibraft-cu12>=24.4.0a0,<=24.4\" \"raft-dask-cu12>=24.4.0a0,<=24.4\" \\\n", " \"dask-cuda>=24.4.0a0,<=24.4\"\n", "!python -m pip install poetry" ] }, { "cell_type": "code", "execution_count": 1, "id": "30d46c7e", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:14.887002Z", "start_time": "2024-03-18T05:08:14.745818Z" }, "id": "30d46c7e", "tags": [] }, "outputs": [], "source": [ "import os # Miscellaneous operating system interfaces\n", "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", "\n", "from os.path import join # path joining\n", "from pathlib import Path # path joining\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import sklearn as sk\n", "from sklearn.cluster import KMeans\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import regex as re\n", "from scipy.cluster import hierarchy as sch\n", "\n", "import datetime\n", "import time\n", "import timeit\n", "import json\n", "import pickle\n", "\n", "import copy\n", "import random\n", "from itertools import chain\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "24c592bd-126b-4390-ac01-6ed901867937", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/javascript": "if (!(\"Notification\" in window)) {\n alert(\"This browser does not support desktop notifications, so the %%notify magic will not work.\");\n} else if (Notification.permission !== 'granted' && Notification.permission !== 'denied') {\n Notification.requestPermission(function (permission) {\n if(!('permission' in Notification)) {\n Notification.permission = permission;\n }\n })\n}\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext jupyternotify" ] }, { "cell_type": "code", "execution_count": 3, "id": "9d2d389d", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:19.008418Z", "start_time": "2024-03-18T05:08:18.766986Z" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "9d2d389d", "outputId": "b4c57902-7420-4ac9-af9b-30f85a223aec", "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import nltk\n", "nltk.download('wordnet')\n", "nltk.download('punkt')" ] }, { "cell_type": "code", "execution_count": 4, "id": "21649155", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:23.357839Z", "start_time": "2024-03-18T05:08:22.163648Z" }, "id": "21649155", "tags": [] }, "outputs": [], "source": [ "import textblob\n", "from textblob import TextBlob\n", "from textblob.wordnet import Synset\n", "from textblob import Word\n", "from textblob.wordnet import VERB" ] }, { "cell_type": "code", "execution_count": 5, "id": "e053a473fa06cc20", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:26.169052Z", "start_time": "2024-03-18T05:08:26.126023Z" }, "collapsed": false, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/notebooks/TopicModelingRepo/BERTopic\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/root/anaconda3/envs/newpy/lib/python3.11/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n", " self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n" ] } ], "source": [ "%cd /workspace/TopicModelingRepo/BERTopic" ] }, { "cell_type": "code", "execution_count": 6, "id": "73f73f3e1f6aec74", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:29.850040Z", "start_time": "2024-03-18T05:08:29.806803Z" }, "collapsed": false, "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 7, "id": "41565640", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:32.847983Z", "start_time": "2024-03-18T05:08:32.807172Z" }, "id": "41565640", "tags": [] }, "outputs": [], "source": [ "from bertopic import BERTopic\n", "from bertopic.vectorizers import ClassTfidfTransformer\n", "from bertopic.representation import KeyBERTInspired, MaximalMarginalRelevance\n", "from sklearn.datasets import fetch_20newsgroups\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sentence_transformers import SentenceTransformer\n", "# from cuml.manifold import UMAP\n", "# from umap import UMAP\n", "# from hdbscan import HDBSCAN\n", "from cuml.cluster import HDBSCAN\n", "from cuml.manifold import UMAP" ] }, { "cell_type": "code", "execution_count": 8, "id": "47517afa", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:36.277280Z", "start_time": "2024-03-18T05:08:36.234932Z" }, "id": "47517afa", "tags": [] }, "outputs": [], "source": [ "# working_dir = os.path.abspath(os.getcwd()) # path to current working directory\n", "working_dir = os.path.abspath(os.path.join(\"/workspace\", \"TopicModelingRepo\"))\n", "data_dir = os.path.join(working_dir, 'data')\n", "lib_dir = os.path.join(working_dir, 'libs')\n", "outer_output_dir = os.path.join(working_dir, 'outputs')\n", "\n", "output_dir_name = time.strftime('%Y_%m_%d')\n", "# output_dir_name = time.strftime('2024_03_23')\n", "\n", "output_dir = os.path.join(outer_output_dir, output_dir_name)\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", "\n", "stopwords_path = os.path.join(data_dir, 'vietnamese_stopwords_dash.txt')\n", "\n", "\n", "# profile_news_path = os.path.join(output_dir, \"profile_news.html\")\n", "# profile_comments_path = os.path.join(output_dir, \"profile_comments.html\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "c8eb5d63", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T09:53:53.247342Z", "start_time": "2024-03-12T09:53:52.411723Z" }, "id": "c8eb5d63" }, "outputs": [], "source": [ "# # doc_source = 'tripadvisor'\n", "# # doc_time = '2023_Dec_25'\n", "# doc_source = 'vntourism_merge'\n", "# doc_time = '2024_Jan_15'\n", "# # doc_source = 'merge'\n", "# # doc_time = '2024_Jan_22'\n", "# doc_type = 'reviews'\n", "# doc_level = 'sentence'\n", "\n", "# zeroshot_topic_list = [\"Amenity\",\"Branding\",\"Experience\",\"Facility\",\"Loyalty\",\"Service\"]\n", "# # zeroshot_topic_list = [\"brand\",\"reputation\",\"expectation\",\"disappoint\",\"overated\",\n", "# # \"underated\",\"underdeliver\",\"promises\"]\n", "\n", "# # Limit when looping over the numbers of topics\n", "# # limit=11; start=10; step=1;\n", "\n", "# # Collect file df_en_review_vntourism\n", "# if doc_source == 'vntourism_merge':\n", "# if doc_time == '2024_Jan_15':\n", "# date_col = 'Date'\n", "# df_reviews_path = os.path.join(data_dir, 'df_en_review_vntourism.csv')\n", "# df_reviews = pd.read_csv(df_reviews_path, lineterminator='\\n')\n", "# df_reviews = df_reviews.loc[df_reviews['year']>0]\n", "# # df_reviews = df_reviews.sample(n=100000,random_state=0)\n", "\n", "# if doc_type == 'reviews':\n", "# df_doc = df_reviews\n", "# df_doc['dates'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('M').\\\n", "# dt.strftime('%Y-%m-%d')\n", "# # timestamps = df_doc['dates'].to_list()\n", "# # df_doc = df_doc.loc[(df_doc['dates']>='2020-04-01') & (df_doc['dates']<'2022-01-01')]\n", "\n", "# df_doc['dates_yearly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('Y').\\\n", "# dt.strftime('%Y')\n", "\n", "# df_doc['dates_quarterly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('d').\\\n", "# dt.strftime('%YQ%q')\n", "\n", "# df_doc['dates_monthly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('M').\\\n", "# dt.strftime('%Y-%m')\n", "\n", "# timestamps_dict = dict()\n", "# timestamps_dict['yearly'] = df_doc['dates_yearly'].to_list()\n", "# timestamps_dict['quarterly'] = df_doc['dates_quarterly'].to_list()\n", "# timestamps_dict['monthly'] = df_doc['dates_monthly'].to_list()\n", "# timestamps_dict['date'] = df_doc['dates'].to_list()\n", "\n", "# target_col = 'normalized_content'\n", "# df_documents = df_doc[target_col]\n", "\n", "\n", "# # Collect file merge_final\n", "# elif doc_source == 'merge':\n", "# if doc_time == '2024_Jan_22':\n", "# date_col = 'Date'\n", "# df_reviews_path = os.path.join(data_dir, 'merge_final.csv')\n", "# df_reviews = pd.read_csv(df_reviews_path, lineterminator='\\n')\n", "# df_reviews = df_reviews.loc[df_reviews['year']>0]\n", "# # df_reviews = df_reviews.sample(n=100000,random_state=0)\n", "# # df_reviews = df_reviews.loc[df_reviews['source'] == 'tripadvisor']\n", "# df_reviews = df_reviews.loc[df_reviews['language'] == 'English']\n", "\n", "# if doc_type == 'reviews':\n", "# df_doc = df_reviews\n", "# df_doc['dates'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('M').\\\n", "# dt.strftime('%Y-%m-%d')\n", "# # timestamps = df_doc['dates'].to_list()\n", "# # df_doc = df_doc.loc[(df_doc['dates']>='2020-04-01') & (df_doc['dates']<'2022-01-01')]\n", "\n", "# df_doc['dates_yearly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('Y').\\\n", "# dt.strftime('%Y')\n", "\n", "# df_doc['dates_quarterly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('d').\\\n", "# dt.strftime('%YQ%q')\n", "\n", "# df_doc['dates_monthly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce').\\\n", "# dt.to_period('M').\\\n", "# dt.strftime('%Y-%m')\n", "\n", "# timestamps_dict = dict()\n", "# timestamps_dict['yearly'] = df_doc['dates_yearly'].to_list()\n", "# timestamps_dict['quarterly'] = df_doc['dates_quarterly'].to_list()\n", "# timestamps_dict['monthly'] = df_doc['dates_monthly'].to_list()\n", "# timestamps_dict['date'] = df_doc['dates'].to_list()\n", "\n", "# target_col = 'normalized_content'\n", "# df_documents = df_doc[target_col]\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "4b8c81ecb7027dfd", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:08:45.671191Z", "start_time": "2024-03-18T05:08:45.623832Z" }, "collapsed": false, "tags": [] }, "outputs": [], "source": [ "# Create dataframe\n", "doc_time = '2024_Jan_15'\n", "# doc_time = '2024_Jan_22'\n", "doc_type = 'reviews'\n", "doc_level = 'sentence'\n", "\n", "# zeroshot_topic_list = [\"Amenity\",\"Branding\",\"Experience\",\"Facility\",\"Loyalty\",\"Service\"]\n", "\n", "\n", "def prepare_data(doc_source, doc_type, type_framework = 'pandas'):\n", " name_file = doc_source.split('.')[0]\n", " out_dir = os.path.join(output_dir, name_file)\n", " if not os.path.exists(out_dir):\n", " os.makedirs(out_dir)\n", "\n", " date_col = 'Date'\n", " df_reviews_path = os.path.join(data_dir, doc_source)\n", "\n", " if type_framework == 'pandas':\n", " df_reviews = pd.read_csv(df_reviews_path, lineterminator='\\n', encoding='utf-8') # Pandas\n", " df_reviews = df_reviews.loc[df_reviews['year']>0] # Pandas\n", " df_reviews = df_reviews.loc[df_reviews['language'] == 'English'] # Pandas\n", "\n", " if doc_type == 'reviews':\n", " df_doc = df_reviews\n", " df_doc['dates'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \\\n", " dt.to_period('M'). \\\n", " dt.strftime('%Y-%m-%d') # pandas\n", "\n", "\n", " # timestamps = df_doc['dates'].to_list()\n", " # df_doc = df_doc.loc[(df_doc['dates']>='2020-04-01') & (df_doc['dates']<'2022-01-01')]\n", "\n", " df_doc['dates_yearly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \\\n", " dt.to_period('Y'). \\\n", " dt.strftime('%Y') # pandas\n", "\n", "\n", " df_doc['dates_quarterly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \\\n", " dt.to_period('d'). \\\n", " dt.strftime('%YQ%q') # pandas\n", "\n", "\n", " df_doc['dates_monthly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \\\n", " dt.to_period('M'). \\\n", " dt.strftime('%Y-%m')\n", "\n", " elif type_framework == 'polars':\n", " df_reviews = pl.read_csv(df_reviews_path, separator='\\n') # Polars\n", " df_reviews = df_reviews.filter(pl.col(\"year\")>0) # Polars\n", " df_reviews = df_reviews.filter(pl.col('language') == 'English') # Polars\n", "\n", " if doc_type == 'reviews':\n", " df_doc = df_reviews\n", "\n", " df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \\\n", " to_period('M'). \\\n", " strftime('%Y-%m-%d').alias('dates')) # polars\n", "\n", " df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \\\n", " to_period('Y'). \\\n", " strftime('%Y').alias('dates_yearly')) # polars\n", "\n", "\n", " df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \\\n", " to_period('d'). \\\n", " strftime('%YQ%q').alias('dates_quarterly')) # polars\n", "\n", "\n", " df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \\\n", " to_period('M'). \\\n", " strftime('%Y-%m').alias('dates_monthly')) # polars\n", "\n", "\n", " timestamps_dict = dict()\n", " timestamps_dict['yearly'] = df_doc['dates_yearly'].to_list()\n", " timestamps_dict['quarterly'] = df_doc['dates_quarterly'].to_list()\n", " timestamps_dict['monthly'] = df_doc['dates_monthly'].to_list()\n", " timestamps_dict['date'] = df_doc['dates'].to_list()\n", "\n", " target_col = 'normalized_content'\n", " df_documents = df_doc[target_col]\n", "\n", " return (timestamps_dict, df_doc, df_documents, df_reviews)" ] }, { "cell_type": "code", "execution_count": 10, "id": "2b0fc3e4d4fd8f9b", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:09:12.107207Z", "start_time": "2024-03-18T05:09:00.938380Z" }, "collapsed": false, "tags": [] }, "outputs": [ { "data": { "application/javascript": "$(document).ready(\n function() {\n function appendUniqueDiv(){\n // append a div with our uuid so we can check that it's already\n // been sent and avoid duplicates on page reload\n var notifiedDiv = document.createElement(\"div\")\n notifiedDiv.id = \"36c98ea0-a481-4f94-974d-2478a4b4c409\"\n element.append(notifiedDiv)\n }\n\n // only send notifications if the pageload is complete; this will\n // help stop extra notifications when a saved notebook is loaded,\n // which during testing gives us state \"interactive\", not \"complete\"\n if (document.readyState === 'complete') {\n // check for the div that signifies that the notification\n // was already sent\n if (document.getElementById(\"36c98ea0-a481-4f94-974d-2478a4b4c409\") === null) {\n var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell execution has finished!\"};\n if (Notification.permission !== 'denied') {\n if (Notification.permission !== 'granted') { \n Notification.requestPermission(function (permission) {\n if(!('permission' in Notification)) {\n Notification.permission = permission\n }\n })\n }\n if (Notification.permission === 'granted') {\n var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n appendUniqueDiv()\n notification.onclick = function () {\n window.focus();\n this.close();\n };\n } \n } \n }\n }\n }\n)\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%notify\n", "\n", "# Create dataframe for dataset booking\n", "doc_source = 'en_booking(fixed).csv'\n", "list_tmp = prepare_data(doc_source, doc_type, type_framework = 'pandas')\n", "\n", "(timestamps_dict_booking, df_doc_booking,\n", " df_documents_booking, df_reviews_booking) = list_tmp\n", "\n", "\n", "# Create dataframe for dataset tripadvisor\n", "doc_source = 'en_tripadvisor(fixed).csv'\n", "list_tmp = prepare_data(doc_source, doc_type, type_framework = 'pandas')\n", "(timestamps_dict_tripadvisor, df_doc_tripadvisor,\n", " df_documents_tripadvisor, df_reviews_tripadvisor) = list_tmp" ] }, { "cell_type": "code", "execution_count": 11, "id": "31e9a85237b67bdd", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T02:56:52.821064Z", "start_time": "2024-03-18T02:56:52.810285Z" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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id_urlDatemonthyearnormalized_scorenormalized_titlenormalized_contentlanguage_codelanguagenationalityhotel_namehotel_addresshotel_starprovincesourcelinkdatesdates_yearlydates_quarterlydates_monthly
0a-phu-homes2022-06620228.0Rất tốtThe staff were very kind and helpful. The dog ...enEnglishVietnamA phủ home’sTà Xùa, Bắc Yên, Sơn La, Bắc Yên, Việt Nam0-starSơn LaBookinghttps://www.booking.com/hotel/vn/a-phu-homes.v...2022-06-3020222022Q22022-06
1cloud-forest-ta-xua-son-la2023-077202310.0Lovely placeLocation is superb, indoor decor with live pla...enEnglishItalyCloud forest tà xùaCloud Forest Tà Xùa, Khu Đỉnh Gió, Xã Tà Xùa, ...0-starSơn LaBookinghttps://www.booking.com/hotel/vn/cloud-forest-...2023-07-3120232023Q32023-07
2dream-garden-khu-sinh-thai-vuon-mo2023-05520233.0Kémsmelly roomaccess going to the room is a bit h...enEnglishVietnamDream garden (khu sinh thái vườn mơ)196 Quốc lộ 6, Mộc Châu, Việt Nam0-starSơn LaBookinghttps://www.booking.com/hotel/vn/dream-garden-...2023-05-3120232023Q22023-05
3galaxy-grand2023-08820239.0Lovely hotel, proximity to everything!The rooms were very nice, clean and spacious. ...enEnglishCanadaGalaxy grand hotel228 đường Trường Chinh, Thành phố Sơn La Số 22...3-starSơn LaBookinghttps://www.booking.com/hotel/vn/galaxy-grand....2023-08-3120232023Q32023-08
4galaxy-grand2022-111120228.0Rất tốtAt this hotel I had the most boring breakfast ...enEnglishVietnamGalaxy grand hotel228 đường Trường Chinh, Thành phố Sơn La Số 22...3-starSơn LaBookinghttps://www.booking.com/hotel/vn/galaxy-grand....2022-11-3020222022Q42022-11
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1https://www.tripadvisor.com.vn/Hotel_Review-g2...2023-05520235.0Great place in Cau GiayThis place was very nice. Our bedroom were cle...enEnglishVietnam22land residence hotel02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V...3-starHà Nộitripadvisorhttps://www.tripadvisor.com.vn/Hotel_Review-g2...2023-05-3120232023Q22023-05
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Unnamed: 0sentencereview_iddatemonthlyquarterlyyearly
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44Exceptional value for money and I couldn't rec...22022-07-312022-072022Q32022
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4916644916643 restaurants and 24 hour room service provide...858502017-08-312017-082017Q32017
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3galaxy-grand2023-08820239.0Lovely hotel, proximity to everything!The rooms were very nice, clean and spacious. ...enEnglishCanadaGalaxy grand hotel228 đường Trường Chinh, Thành phố Sơn La Số 22...3-starSơn LaBookinghttps://www.booking.com/hotel/vn/galaxy-grand....2023-08-3120232023Q32023-08
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1https://www.tripadvisor.com.vn/Hotel_Review-g2...2023-05520235.0Great place in Cau GiayThis place was very nice. Our bedroom were cle...enEnglishVietnam22land residence hotel02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V...3-starHà Nộitripadvisorhttps://www.tripadvisor.com.vn/Hotel_Review-g2...2023-05-3120232023Q22023-05
2https://www.tripadvisor.com.vn/Hotel_Review-g2...2022-121220225.0Perfect stayIt was a amazing hotel. They helped very good ...enEnglishNetherlands22land residence hotel02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V...3-starHà Nộitripadvisorhttps://www.tripadvisor.com.vn/Hotel_Review-g2...2022-12-3120222022Q42022-12
3https://www.tripadvisor.com.vn/Hotel_Review-g2...2022-09920225.0Wonderful and comfortable place to stayI was amazed at the hotel. The room was beauti...enEnglishUSA22land residence hotel02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V...3-starHà Nộitripadvisorhttps://www.tripadvisor.com.vn/Hotel_Review-g2...2022-09-3020222022Q32022-09
4https://www.tripadvisor.com.vn/Hotel_Review-g2...2019-05520195.0BussinessLocation great, rooms were big and spacious. N...enEnglishno_info22 residence hanoi22 Nam Ngư Cửa Nam, Hoàn Kiếm, Hà Nội 100000 V...3-starHà Nộitripadvisorhttps://www.tripadvisor.com.vn/Hotel_Review-g2...2019-05-3120192019Q22019-05
\n", "
" ], "text/plain": [ " id_url Date month year \\\n", "0 https://www.tripadvisor.com.vn/Hotel_Review-g2... 2023-07 7 2023 \n", "1 https://www.tripadvisor.com.vn/Hotel_Review-g2... 2023-05 5 2023 \n", "2 https://www.tripadvisor.com.vn/Hotel_Review-g2... 2022-12 12 2022 \n", "3 https://www.tripadvisor.com.vn/Hotel_Review-g2... 2022-09 9 2022 \n", "4 https://www.tripadvisor.com.vn/Hotel_Review-g2... 2019-05 5 2019 \n", "\n", " normalized_score normalized_title \\\n", "0 5.0 Very good hotel \n", "1 5.0 Great place in Cau Giay \n", "2 5.0 Perfect stay \n", "3 5.0 Wonderful and comfortable place to stay \n", "4 5.0 Bussiness \n", "\n", " normalized_content language_code language \\\n", "0 Good hotel i have ever stayed in Vietnam, good... en English \n", "1 This place was very nice. Our bedroom were cle... en English \n", "2 It was a amazing hotel. They helped very good ... en English \n", "3 I was amazed at the hotel. The room was beauti... en English \n", "4 Location great, rooms were big and spacious. N... en English \n", "\n", " nationality hotel_name \\\n", "0 no_info 20 hotel and apartment \n", "1 Vietnam 22land residence hotel \n", "2 Netherlands 22land residence hotel \n", "3 USA 22land residence hotel \n", "4 no_info 22 residence hanoi \n", "\n", " hotel_address hotel_star province \\\n", "0 93A Đội Cấn, Ba Dinh, Hà Nội 100000 Việt Nam 3-star Hà Nội \n", "1 02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V... 3-star Hà Nội \n", "2 02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V... 3-star Hà Nội \n", "3 02 Nguyen Dinh Hoan, Cau Giay, Hà Nội 100000 V... 3-star Hà Nội \n", "4 22 Nam Ngư Cửa Nam, Hoàn Kiếm, Hà Nội 100000 V... 3-star Hà Nội \n", "\n", " source link dates \\\n", "0 tripadvisor https://www.tripadvisor.com.vn/Hotel_Review-g2... 2023-07-31 \n", "1 tripadvisor https://www.tripadvisor.com.vn/Hotel_Review-g2... 2023-05-31 \n", "2 tripadvisor https://www.tripadvisor.com.vn/Hotel_Review-g2... 2022-12-31 \n", "3 tripadvisor https://www.tripadvisor.com.vn/Hotel_Review-g2... 2022-09-30 \n", "4 tripadvisor https://www.tripadvisor.com.vn/Hotel_Review-g2... 2019-05-31 \n", "\n", " dates_yearly dates_quarterly dates_monthly \n", "0 2023 2023Q3 2023-07 \n", "1 2023 2023Q2 2023-05 \n", "2 2022 2022Q4 2022-12 \n", "3 2022 2022Q3 2022-09 \n", "4 2019 2019Q2 2019-05 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_doc_tripadvisor.head()" ] }, { "cell_type": "code", "execution_count": 11, "id": "c281d491", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:09:20.480076Z", "start_time": "2024-03-18T05:09:20.437218Z" }, "id": "c281d491" }, "outputs": [], "source": [ "# random.seed(1)\n", "target_col = 'normalized_content'\n", "\n", "def flatten_comprehension(matrix):\n", " return [item for row in matrix for item in row]\n", "\n", "def processing_data(df_doc, df_documents, timestamps_dict, doc_level, target_col):\n", "\n", " if doc_level == 'sentence':\n", " # num_sent = [len(TextBlob(row).sentences) for row in df_doc[target_col]]\n", " # df_documents = pd.Series(flatten_comprehension([[str(sentence) for sentence in TextBlob(row).sentences] for row in df_documents]))\n", "\n", " # Split sentence which \".\"\n", " ll_sent = [[str(sent) for sent in nltk.sent_tokenize(row,language='english')] for row in df_doc[target_col]]\n", "\n", " # Count number sentence for each comment\n", " num_sent = [len(x) for x in ll_sent]\n", "\n", " # Flat m' sentence in N comment to m'*N comment\n", " df_documents = pd.Series(flatten_comprehension([x for x in ll_sent]))\n", "\n", " # timestamps = list(chain.from_iterable(n*[item] for item, n in zip(timestamps, num_sent)))\n", "\n", " # Copy timestamp features to number sentence times for each comment and flatten them adopt with new m'*N comment\n", " for key in timestamps_dict.keys():\n", " timestamps_dict[key] = list(chain.from_iterable(n*[item] for item, n in zip(timestamps_dict[key], num_sent)))\n", " # time_slice = df_doc['year'].value_counts().sort_index().tolist()\n", " # time_slice = np.diff([np.cumsum(num_sent)[n-1] for n in np.cumsum(time_slice)],prepend=0).tolist()\n", " # elif doc_level == 'whole':\n", " # df_documents\n", "\n", " # Copy id features to number sentence times for each comment and flatten them adopt with new m'*N comment\n", " sent_id_ll = [[j]*num_sent[i] for i,j in enumerate(df_doc.index)]\n", " sent_id = flatten_comprehension(sent_id_ll)\n", "\n", " # Define a new data frame with new m'*N comment\n", " df_doc_out = pd.DataFrame({\n", " 'sentence':df_documents, 'review_id':sent_id,\n", " 'date':timestamps_dict['date'],\n", " 'monthly':timestamps_dict['monthly'],\n", " 'quarterly':timestamps_dict['quarterly'],\n", " 'yearly':timestamps_dict['yearly']})\n", "\n", "\n", " return df_documents, timestamps_dict, sent_id, df_doc_out" ] }, { "cell_type": "code", "execution_count": 12, "id": "1d259f752335767a", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:10:29.101059Z", "start_time": "2024-03-18T05:09:23.384931Z" }, "collapsed": false }, "outputs": [ { "data": { "application/javascript": "$(document).ready(\n function() {\n function appendUniqueDiv(){\n // append a div with our uuid so we can check that it's already\n // been sent and avoid duplicates on page reload\n var notifiedDiv = document.createElement(\"div\")\n notifiedDiv.id = \"eac030da-c337-4a32-863f-0afcf7741540\"\n element.append(notifiedDiv)\n }\n\n // only send notifications if the pageload is complete; this will\n // help stop extra notifications when a saved notebook is loaded,\n // which during testing gives us state \"interactive\", not \"complete\"\n if (document.readyState === 'complete') {\n // check for the div that signifies that the notification\n // was already sent\n if (document.getElementById(\"eac030da-c337-4a32-863f-0afcf7741540\") === null) {\n var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell execution has finished!\"};\n if (Notification.permission !== 'denied') {\n if (Notification.permission !== 'granted') { \n Notification.requestPermission(function (permission) {\n if(!('permission' in Notification)) {\n Notification.permission = permission\n }\n })\n }\n if (Notification.permission === 'granted') {\n var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n appendUniqueDiv()\n notification.onclick = function () {\n window.focus();\n this.close();\n };\n } \n } \n }\n }\n }\n)\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%notify\n", "# Processing data for booking dataset\n", "(df_documents_booking, timestamps_dict_booking,\n", " sent_id_booking, df_doc_out_booking) = processing_data(df_doc_booking, df_documents_booking, timestamps_dict_booking, doc_level, target_col)\n", "\n", "# Processing data for tripadvisor dataset\n", "(df_documents_tripadvisor, timestamps_dict_tripadvisor,\n", " sent_id_tripadvisor, df_doc_out_tripadvisor) = processing_data(df_doc_tripadvisor, df_documents_tripadvisor, timestamps_dict_tripadvisor, doc_level, target_col)" ] }, { "cell_type": "code", "execution_count": 41, "id": "9d866f6d", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T02:58:58.722322Z", "start_time": "2024-03-18T02:58:58.716174Z" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "9d866f6d", "outputId": "556d39ee-00ae-4f18-9631-6ea8a93ac77b" }, "outputs": [ { "data": { "text/plain": [ "0 The staff were very kind and helpful.\n", "1 The dog was also a sweetheart.\n", "2 The water pressure in the shower was weak.\n", "3 The bed mat was also hard, so it was hard for ...\n", "4 Location is superb, indoor decor with live pla...\n", " ... \n", "938449 No\n", "938450 staff was great room door allows light from ha...\n", "938451 This is a quick fix that should be done.\n", "938452 Payment method should be clarified as I was un...\n", "938453 com or pay direct to hotel.\n", "Length: 938454, dtype: object" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_documents_booking" ] }, { "cell_type": "code", "execution_count": 17, "id": "29512f0544fe42b1", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T02:59:02.371551Z", "start_time": "2024-03-18T02:59:02.366422Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 Good hotel i have ever stayed in Vietnam, good...\n", "1 i will be back soon .\n", "2 This place was very nice.\n", "3 Our bedroom were clean and comfortable.\n", "4 The bathtub was great and something you won’t ...\n", " ... \n", "3365406 The staff was very nice, spoke good English an...\n", "3365407 My friend and I received excellent and profess...\n", "3365408 Always with a smile for the customers.\n", "3365409 Good place to sleep in Da Nang, they have also...\n", "3365410 Cheap and satisfied hostel!\n", "Length: 3365411, dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_documents_tripadvisor" ] }, { "cell_type": "code", "execution_count": 60, "id": "25f1aa01", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:10:41.145561Z", "start_time": "2024-03-18T05:10:41.102428Z" }, "colab": { "base_uri": "https://localhost:8080/", "height": 337, "referenced_widgets": [ "1f8366fe67124b138783181e8bdb2fc7", "609abc5c3d524aaba2078143371a7f72", "8c2c918c48d74e72985b6f21d5595bc2", "ee361030eb274f2bb5f48f669dceca4e", "c3c33ca2b4e644f88b11d9f30f99d184", "6d5c243d23194449841f2eb242d26a63", "a1eb95ecedc6421dbf3755f07b7287f1", "598cf526e0bf40af8f86dbbc715143c3", "bedcd18a84b54b67bd6d04c87b8d5a00", "7bbfa522d46044e697bbb4e9484cf22f", "3923be2681f6455fa1ceb420d7c9e0fc", "75dfb46db30244269b4dff387f6581ce", "e9e81918be214e4abb309700e51cc12b", "afcb9baa5e7942a484f9719dcf3e3044", "7b1ad42320d249cc827b50193d1e6014", "c7c67f521c5f44c594056142c65cf768", "400a4adb195b40c4b377b240c45e98c1", "081b0c10ae4a427d9b62808ddef870b1", "411078b7c7b34eae96a347250fff7fb5", "98089a328fd649e1aac43aac17874f50", "1a52b094d00349dcac270211d92de66e", "5d4a77c2971d4769824c2d94460e4e65", "04eea9d71a2f4268ab9cfae4ab045e1c", "446ad8b83f4c4da79e27e5e11c39d94a", "774b1c05754c4c549f7a600f6f9a6979", "347d2f6641134e3c8e13d48d4b4c6324", "b8625d4f5038490fb3eec996ae89637b", "2e14c09f86c940f0a038d18d0b6af62f", "4cf2c11c889348329ce6e8dc3f4fa300", "eba7deac095a4be6abefd779bba34721", "40c6e6560a1541c3948d11c520140750", "d16f61f2930842499f88e8e1bbb7416d", "6626f643e376420db1639fe58247c7c4", "4ec0239c955f429d85d10632b7c45d01", "05578110ac66410eacab842741a74360", "409b8c99f6d94c64ae641af9c19d6542", "06e886eac6474f8792d2534c0010626e", "f60a1fddd3e0498089a6f31a19f77e0a", "61cac86ecec04a39be9dc1095b665746", "a209e63408364ac9aa690f5b068458f4", "1e082a8585954fcf9d75d94a36fe1a97", "b49a305797ee42da80f9753997998d57", "9769e762390f421aba7986d309be0e74", "b52026af1c0447bbb361a7f133edfb5e", "d84898edaf524a37be78c35fed017fbc", "9149e81b8943484985445705b1fb3377", "8e587f7bf9254a3aafd6b8b8f3ad82d7", "d6a042d1ad8142698eeaef1be31a031b", "39b60f3f82b442219d23a8b298391846", "21729391f5eb4af38b2afd8c0a62cd62", "eaa05d5f0e924b12a30107d9a6f3d929", "ecaa6b34a62f4881993edf7e1262af86", "3f134c1958a842afac799a42e54eaa17", "4a2fcf13926544f5822164baaf173449", "31325e1f3d054a779e5081bf1ae3f5e9", "1b583f844a3f404ca379273e8b9a74c7", "40669ba642e740649b088ffbd4e34ab9", "52f2ab7f99a849c780953baa0527feee", "633f34bfbde4486b8267df580125b208", "c0d771439dd14b04b80f3891e6a69f9b", "ede91dc91bc4413db7708b427f0663a9", "d2f920a41ffc46e88b843b83ff3ea2fc", "3d184fea38b54e55895c205e96da3bf7", "df9c8e27a8c44f0b82e3196f3427e525", "433f003a231e4698b570256a49634a6f", "17a713f33d3743228435f2a08d809348", "caa6bd1e62b64fb7b4dd4ecf7f92dc31", "9786e8fc64894c8caa5790b4ef7c8b4b", "ef0a107a2e5540b0ad5013f0a94e11db", "27542482fc95484a8a9fca93cb6ca8a1", "9b11f476bd254780b8103fb1fcf908ef", "c9ec2ed7418748a0b717186941673bc7", "596cb21e3acd49c8995d51c5a4f80c05", "af8fd2114daf4a89bca485cf472176a6", "b5f538b251a7424c82e7ba996c01f6f2", "a41e6c7b6e5d470580f47014d4e6c251", "1fd90a7b98604bb1967d403f7e0fa1e7", "d1da3fc43f49434db39d17b4f305cecb", "3d7beec5c583488fbed8c32f8bf34067", "548332bd21714163969717c85f7a6fee", "2d3ebe2b8cb44334a2f19b1bd00e4eb4", "95fa2ddf2b984a6cbd1d279cb362c2b1", "25c692020f2a496fbd73ffd80bfd880c", "a8319e124de044298c7f6b9214b12727", "46591eff7cca40ed9465603aa0096f6d", "c51f747b38664c2ba38bc089531dc0e4", "74d7dd4870b7421abde6178fe9362b67", "003192a2f2f9494993c83613d015d876", "2f613def580847cf9ec4d2bf80c2720c", "94f33868c6cf4e488ae3813b187b90a8", "5d07fe7ad6d94f21b8156d8076c640d0", "36d8d2dcf30c40158fbdca2ad36c1397", "b3625f84a455406fb267f9534878bf73", "f948b6b57ae845d39d822f9ab0666282", "daed1819542940a48cc3cff58d728f47", "f40a768a944b4291aa27a6f191417b80", "5a703e034364456194254aea5eaff505", "3064a60178354220aa48e0466852a9f6", "ce66b381810244f38da499da94033b1c", "66766c0d26644a7e97caedf60f40992e", "9266ceeddf0f49849b6ebbc6c85402af", "87438bd8b83442f99fea09e5764ec1d4", "0cda56fb8e05470c86f630ffd4e71b8a", "cc337937969043d1905acc5df6346fab", "1578de759b3d4b919246943df903697d", "1e429886b36648daa2c44ad0b4fab69b", "a7b90af5a8884162a0a36e014ccacd9d", "d1241181156248e9932fe6e57d5dce48", "65e653be3b0948eda9131e8ef23121dd", "e814e635bae143f2b2b19e0d67b76f4d" ] }, "id": "25f1aa01", "outputId": "2f2cf436-12e9-4e61-be84-8ae8ca136be4" }, "outputs": [], "source": [ "\n", "def create_model_bertopic():\n", " # sentence_model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n", " # sentence_model = SentenceTransformer(\"all-mpnet-base-v2\")\n", " # sentence_model = SentenceTransformer(\"distiluse-base-multilingual-cased_v2\")\n", " sentence_model = SentenceTransformer(\"thenlper/gte-small\")\n", "\n", " # Get 50 neighbor datapoints and 10 dimensional with metric distance: euclidean\n", " umap_model = UMAP(n_neighbors=50, n_components=10,\n", " min_dist=0.0, metric='euclidean',\n", " low_memory=True,\n", " random_state=1)\n", "\n", "\n", " cluster_model = HDBSCAN(min_cluster_size=50, metric='euclidean',\n", " cluster_selection_method='leaf',\n", " # cluster_selection_method='eom',\n", " prediction_data=True,\n", " leaf_size=20,\n", " min_samples=10)\n", "\n", "\n", " # cluster_model = AgglomerativeClustering(n_clusters=11)\n", " vectorizer_model = CountVectorizer(min_df=1,ngram_range=(1, 1),stop_words=\"english\")\n", " ctfidf_model = ClassTfidfTransformer()\n", " # representation_model = KeyBERTInspired()\n", "\n", " # Diversity param is lambda in equation of Maximal Marginal Relevance\n", " representation_model = MaximalMarginalRelevance(diversity=0.7,top_n_words=10)\n", "\n", "\n", " # Create model\n", " topic_model = BERTopic(embedding_model=sentence_model,\n", " umap_model=umap_model,\n", " hdbscan_model=cluster_model,\n", " vectorizer_model=vectorizer_model,\n", " ctfidf_model=ctfidf_model,\n", " representation_model=representation_model,\n", " # zeroshot_topic_list=zeroshot_topic_list,\n", " # zeroshot_min_similarity=0.7,\n", " nr_topics = 30,\n", " top_n_words = 10,\n", " low_memory=True,\n", " verbose=True)\n", "\n", " return topic_model" ] }, { "cell_type": "markdown", "id": "3a74bcba796da2d4", "metadata": {}, "source": [ "## Topic Modeling for Booking dataset" ] }, { "cell_type": "code", "execution_count": 16, "id": "5efe1eb43ec16f99", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:10:43.852387Z", "start_time": "2024-03-18T05:10:43.811490Z" }, "collapsed": false }, "outputs": [], "source": [ "# Make new directories output of bertopic\n", "# output_subdir_name = 'bertopic_'+doc_type+'_'+doc_level+'_'+doc_time\n", "source = 'en_booking'\n", "output_subdir_name = source + '/bertopic2_non_zeroshot_30topic_'+doc_type+'_'+doc_level+'_'+doc_time\n", "output_subdir = os.path.join(output_dir, output_subdir_name)\n", "if not os.path.exists(output_subdir):\n", " os.makedirs(output_subdir)" ] }, { "cell_type": "code", "execution_count": 61, "id": "c2535ce74db4799e", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:10:51.314094Z", "start_time": "2024-03-18T05:10:47.644156Z" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[I] [02:29:42.157695] Unused keyword parameter: low_memory during cuML estimator initialization\n", "[I] [02:29:42.159809] Unused keyword parameter: leaf_size during cuML estimator initialization\n" ] } ], "source": [ "topic_model_booking = create_model_bertopic()" ] }, { "cell_type": "code", "execution_count": 62, "id": "46b86bd4", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:15:51.495377Z", "start_time": "2024-03-18T05:10:55.900171Z" }, "id": "46b86bd4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-03-26 02:29:46,255 - BERTopic - Embedding - Transforming documents to embeddings.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1fc673f4bb8a46bea5d52a3b4d02582c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/28727 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%notify\n", "\n", "t_start = time.time()\n", "t = time.process_time()\n", "topic_model_booking = topic_model_booking.fit(df_documents_booking)\n", "elapsed_time = time.process_time() - t\n", "t_end = time.time()\n", "print(f'Time working: {t_end - t_start}\\n Time model processing:{elapsed_time}')" ] }, { "cell_type": "code", "execution_count": 19, "id": "87f88126", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:04:53.947781Z", "start_time": "2024-03-18T03:04:53.572867Z" }, "id": "87f88126" }, "outputs": [], "source": [ "topics_save_dir = os.path.join(output_subdir, 'topics_bertopic_'+doc_type+'_'+doc_level+'_'+doc_time)\n", "topic_model_booking.save(topics_save_dir, serialization=\"safetensors\", save_ctfidf=True, save_embedding_model=True)" ] }, { "cell_type": "code", "execution_count": 63, "id": "a37e2017", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:20:27.388198Z", "start_time": "2024-03-18T05:15:51.496949Z" }, "id": "a37e2017", "outputId": "d2719c86-4a04-4d75-e8eb-4eaf6f6ab2f4" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cc395dc305914b9fa31261bfe6ba6875", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/28727 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%notify\n", "\n", "t_start = time.time()\n", "t = time.process_time()\n", "topics, probs = topic_model_booking.transform(df_documents_booking)\n", "elapsed_time = time.process_time() - t\n", "t_end = time.time()\n", "print(f'Time working: {t_end - t_start}\\n Time model processing:{elapsed_time}')" ] }, { "cell_type": "code", "execution_count": 21, "id": "c81cd428", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:09:30.282906Z", "start_time": "2024-03-18T03:09:29.917220Z" }, "id": "c81cd428" }, "outputs": [], "source": [ "topics_save_dir = os.path.join(output_subdir, 'topics_bertopic_transform_'+doc_type+'_'+doc_level+'_'+doc_time)\n", "topic_model_booking.save(topics_save_dir, serialization=\"safetensors\", save_ctfidf=True, save_embedding_model=True)" ] }, { "cell_type": "code", "execution_count": 22, "id": "ea843be6", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:12:41.322287Z", "start_time": "2024-03-18T03:12:41.312212Z" }, "id": "ea843be6", "outputId": "94efc074-4797-41ff-b7ee-c9f45972b979", "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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TopicCountNameRepresentationRepresentative_Docs
0-1784394-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...
10144090_staff_friendly_helpful_owner[staff, friendly, helpful, owner, location, am...[The staff are all so friendly and helpful., T...
21125301_particular_amazing_nothingj_sucks2[particular, amazing, nothingj, sucks2, sile, ...[Nothing really, Nothing in particular., Nothi...
32109502_hostel_hue_nights_nang[hostel, hue, nights, nang, hcmc, visit, hoi, ...[Great hostel., Great hostel., this hostel is ...
4380983_balcony_villa_river_views[balcony, villa, river, views, rice, bungalows...[Had a great view from the balcony as well., G...
..................
95945394_wow_factor_absolute_simply[wow, factor, absolute, simply, , , , , , , , ...[wow!, Wow!, Wow!]
96955195_paradise_slice_photographers_wanderlust[paradise, slice, photographers, wanderlust, t...[It is paradise, Paradise., Such a paradise!]
97965196_liked_competitive_amazingly_designed[liked, competitive, amazingly, designed, pric...[I liked everything, I liked everything, I lik...
98975197_bath_herbal_soak_night[bath, herbal, soak, night, feet, complementar...[Lovely foot bath each evening., Also the free...
99985098_rat_sach_tot_phong[rat, sach, tot, phong, khong, thoai, tien, du...[Cho o sach se nhan vien than thien, Khach san...
\n", "

100 rows × 5 columns

\n", "
" ], "text/plain": [ " Topic Count Name \\\n", "0 -1 784394 -1_staff_hotel_breakfast_helpful \n", "1 0 14409 0_staff_friendly_helpful_owner \n", "2 1 12530 1_particular_amazing_nothingj_sucks2 \n", "3 2 10950 2_hostel_hue_nights_nang \n", "4 3 8098 3_balcony_villa_river_views \n", ".. ... ... ... \n", "95 94 53 94_wow_factor_absolute_simply \n", "96 95 51 95_paradise_slice_photographers_wanderlust \n", "97 96 51 96_liked_competitive_amazingly_designed \n", "98 97 51 97_bath_herbal_soak_night \n", "99 98 50 98_rat_sach_tot_phong \n", "\n", " Representation \\\n", "0 [staff, hotel, breakfast, helpful, beach, serv... \n", "1 [staff, friendly, helpful, owner, location, am... \n", "2 [particular, amazing, nothingj, sucks2, sile, ... \n", "3 [hostel, hue, nights, nang, hcmc, visit, hoi, ... \n", "4 [balcony, villa, river, views, rice, bungalows... \n", ".. ... \n", "95 [wow, factor, absolute, simply, , , , , , , , ... \n", "96 [paradise, slice, photographers, wanderlust, t... \n", "97 [liked, competitive, amazingly, designed, pric... \n", "98 [bath, herbal, soak, night, feet, complementar... \n", "99 [rat, sach, tot, phong, khong, thoai, tien, du... \n", "\n", " Representative_Docs \n", "0 [Lovely breakfast, close to beach and friendly... \n", "1 [The staff are all so friendly and helpful., T... \n", "2 [Nothing really, Nothing in particular., Nothi... \n", "3 [Great hostel., Great hostel., this hostel is ... \n", "4 [Had a great view from the balcony as well., G... \n", ".. ... \n", "95 [wow!, Wow!, Wow!] \n", "96 [It is paradise, Paradise., Such a paradise!] \n", "97 [I liked everything, I liked everything, I lik... \n", "98 [Lovely foot bath each evening., Also the free... \n", "99 [Cho o sach se nhan vien than thien, Khach san... \n", "\n", "[100 rows x 5 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_info = topic_model_booking.get_topic_info()\n", "topic_info" ] }, { "cell_type": "code", "execution_count": 23, "id": "fed97312", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:12:51.766051Z", "start_time": "2024-03-18T03:12:51.760281Z" }, "id": "fed97312" }, "outputs": [], "source": [ "topic_info_path_out = os.path.join(output_subdir, 'topic_info_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "topic_info.to_csv(topic_info_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 26, "id": "82711d71", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:12:55.893782Z", "start_time": "2024-03-18T03:12:55.889532Z" }, "id": "82711d71", "outputId": "39d21250-57b9-4c2f-b505-2ae98362ac12" }, "outputs": [ { "data": { "text/plain": [ "[('villa', 0.034104012136767815),\n", " ('bungalow', 0.025104611600005603),\n", " ('nights', 0.024138612672193586),\n", " ('hostels', 0.014939279731602343),\n", " ('staying', 0.012445864384987427),\n", " ('social', 0.012060795432866587),\n", " ('return', 0.011933507134086892),\n", " ('beautiful', 0.010467638248050594),\n", " ('really', 0.009909978740094067),\n", " ('overall', 0.009067252548875557)]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model_booking.get_topic(0)" ] }, { "cell_type": "code", "execution_count": 20, "id": "b62aa0f8", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:13:01.449981Z", "start_time": "2024-03-18T03:13:01.444499Z" }, "id": "b62aa0f8", "outputId": "69b574a9-68f1-4044-bf4c-06e3cf7808e3", "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'Main': {-1: [('staff', 0.007924954689079467),\n", " ('hotel', 0.00771813491405123),\n", " ('breakfast', 0.007385097085630017),\n", " ('clean', 0.007208198302302375),\n", " ('helpful', 0.006673771698757934),\n", " ('friendly', 0.006665077167811092),\n", " ('pool', 0.006427693196688111),\n", " ('really', 0.006309162419411167),\n", " ('service', 0.0049988648188642746),\n", " ('time', 0.004989705229337492)],\n", " 0: [('owners', 0.022573719072239908),\n", " ('reception', 0.017828867186928332),\n", " ('lovely', 0.016612784988099743),\n", " ('staffs', 0.016458559278243214),\n", " ('super', 0.015970260458454684),\n", " ('welcoming', 0.011304681075967722),\n", " ('hotel', 0.010604381083867498),\n", " ('desk', 0.010278808608496857),\n", " ('attentive', 0.010119587774207283),\n", " ('accommodating', 0.009394345397941525)],\n", " 1: [('balcony', 0.0636927883700686),\n", " ('villa', 0.034299384276266734),\n", " ('river', 0.028251818505531316),\n", " ('views', 0.019158696647743816),\n", " ('rice', 0.017051956483033415),\n", " ('phu', 0.01625300034425125),\n", " ('hcmc', 0.016046511099506906),\n", " ('bungalows', 0.01383251026930911),\n", " ('cat', 0.012331889370628289),\n", " ('amazing', 0.011759987076990084)],\n", " 2: [('write', 0.046825317218086054),\n", " ('add', 0.044389744825235486),\n", " ('good', 0.02785093811619717),\n", " ('claim', 0.012967869359722004),\n", " ('significant', 0.012924434787076052),\n", " ('stood', 0.011193561050773805),\n", " ('welcomeeasy', 0.011116710638666816),\n", " ('sucks2', 0.011116710638666816),\n", " ('averything', 0.011116710638666816),\n", " ('betterstaff', 0.011116710638666816)],\n", " 3: [('location', 0.22628691785985774),\n", " ('value', 0.012909019168056734),\n", " ('fantastic', 0.0101133613797554),\n", " ('best', 0.0018193733373779671),\n", " ('quite', 0.0015637350169487056),\n", " ('spot', 0.0014667617329039843),\n", " ('hula', 0.0013503068442472209),\n", " ('affordable', 0.0012947514920095833),\n", " ('apartement', 0.0011825510857036178),\n", " ('beat', 0.0011443528866677268)],\n", " 4: [('bikes', 0.07219031703534634),\n", " ('scooter', 0.05227097013817149),\n", " ('town', 0.025020193242211875),\n", " ('parking', 0.023521046250425514),\n", " ('explore', 0.016647784440377778),\n", " ('shuttle', 0.015707559623190252),\n", " ('renting', 0.014156775899022003),\n", " ('laundry', 0.012444794684903192),\n", " ('grab', 0.012371440442379054),\n", " ('cheap', 0.011609153100179239)],\n", " 5: [('resort', 0.04366836489673964),\n", " ('hostels', 0.028416076168776543),\n", " ('best', 0.013604340484321884),\n", " ('stayed', 0.011105225888565962),\n", " ('run', 0.010532375543201373),\n", " ('people', 0.010276866627397284),\n", " ('beautiful', 0.010110017817950658),\n", " ('location', 0.00835418280096947),\n", " ('modern', 0.008163214519668612),\n", " ('comfortable', 0.007274838098450235)],\n", " 6: [('buffet', 0.06393457227812513),\n", " ('options', 0.025423064842986124),\n", " ('included', 0.012524957034294841),\n", " ('banana', 0.012507370751018248),\n", " ('staff', 0.01051047546078342),\n", " ('amazing', 0.010498732331862484),\n", " ('beach', 0.010272503918685554),\n", " ('breakfasts', 0.009952473019640409),\n", " ('varied', 0.008246618080301008),\n", " ('menu', 0.007395032639098927)],\n", " 7: [('lobby', 0.035600758236854164),\n", " ('shops', 0.03426397643169896),\n", " ('roof', 0.031538355080299556),\n", " ('lots', 0.02265057591130054),\n", " ('bars', 0.021459796435525213),\n", " ('distance', 0.019472044642351544),\n", " ('convenience', 0.015240286865613822),\n", " ('delicious', 0.014025261196070348),\n", " ('market', 0.013634378735584903),\n", " ('local', 0.012885875668175626)],\n", " 8: [('quarter', 0.13790683744055424),\n", " ('district', 0.04658988045914591),\n", " ('ben', 0.039332886028870126),\n", " ('thanh', 0.03480442875580942),\n", " ('location', 0.033042599281112445),\n", " ('walking', 0.03271201546231014),\n", " ('dragon', 0.029495019770250805),\n", " ('vien', 0.020833037678082544),\n", " ('bui', 0.020725714261811634),\n", " ('imperial', 0.01973577550633373)],\n", " 9: [('birthday', 0.05887297772766743),\n", " ('cake', 0.05773452795799723),\n", " ('experience', 0.03755863067210907),\n", " ('honeymoon', 0.03722404853370681),\n", " ('manager', 0.016861842538556333),\n", " ('overall', 0.015361050586626953),\n", " ('surprised', 0.01518416292826825),\n", " ('ginger', 0.015146492273003919),\n", " ('tours', 0.015022726468565027),\n", " ('welcomed', 0.011508472639950805)],\n", " 10: [('binh', 0.04979868108698252),\n", " ('nang', 0.049241225038427136),\n", " ('vietnam', 0.03832757282908776),\n", " ('phong', 0.030983845775159693),\n", " ('loop', 0.03094120304839903),\n", " ('best', 0.02760107769581634),\n", " ('western', 0.02113711118483291),\n", " ('stay', 0.016954854294466602),\n", " ('breakfast', 0.013572170101362331),\n", " ('tour', 0.011371170613994867)],\n", " 11: [('homestay', 0.1649388777515564),\n", " ('hmong', 0.03171715898001298),\n", " ('felt', 0.031139187944852712),\n", " ('zizi', 0.02803635293243614),\n", " ('lovely', 0.021275520766527674),\n", " ('dinner', 0.01563103154827991),\n", " ('sister', 0.01461711449752153),\n", " ('running', 0.01189019304429591),\n", " ('helpful', 0.01143157908107757),\n", " ('business', 0.010832205807733657)],\n", " 12: [('dorms', 0.03135782115231565),\n", " ('comfortable', 0.028783606337973243),\n", " ('privacy', 0.023540812527477476),\n", " ('small', 0.022763735754365612),\n", " ('spacious', 0.022642197236260413),\n", " ('quiet', 0.01992453618443253),\n", " ('family', 0.015303104848520144),\n", " ('photos', 0.014713749167119518),\n", " ('curtain', 0.010833876626653548),\n", " ('ac', 0.010412450141100767)],\n", " 13: [('beds', 0.1014987105604544),\n", " ('firm', 0.03886557403104773),\n", " ('uncomfortable', 0.034032070695481566),\n", " ('huge', 0.015199752029889374),\n", " ('shower', 0.013416224420616053),\n", " ('aircon', 0.013348208542293193),\n", " ('little', 0.011836456772899564),\n", " ('comfiest', 0.010768618814343161),\n", " ('vietnam', 0.010766693727411968),\n", " ('worked', 0.009728809796036058)],\n", " 14: [('wifi', 0.20998379259325572),\n", " ('smart', 0.04037431713205504),\n", " ('strong', 0.029167620624719292),\n", " ('worked', 0.023193482553749964),\n", " ('television', 0.020362570284360157),\n", " ('unstable', 0.018231433729416803),\n", " ('tube', 0.01794554707170495),\n", " ('poor', 0.01763382839382771),\n", " ('speed', 0.014940221278263747),\n", " ('room', 0.0132491348273232)],\n", " 15: [('elevators', 0.038825745970799484),\n", " ('luggage', 0.028786676019045918),\n", " ('renovation', 0.02464655707212636),\n", " ('fitness', 0.0219272727401668),\n", " ('needs', 0.021006918554783923),\n", " ('steep', 0.02094369326853396),\n", " ('outdated', 0.020744673691530498),\n", " ('steps', 0.01693169892432521),\n", " ('small', 0.015960883658978443),\n", " ('construction', 0.0158498358904184)],\n", " 16: [('stayed', 0.11148037363560662),\n", " ('night', 0.055026587993613756),\n", " ('booked', 0.034242340996288596),\n", " ('days', 0.03241779873214209),\n", " ('definitely', 0.025825326814069165),\n", " ('hospitality', 0.01925888049653028),\n", " ('thanks', 0.0181724697922344),\n", " ('extending', 0.014084845828635662),\n", " ('pleasant', 0.013563416958257113),\n", " ('return', 0.012903600229715477)],\n", " 17: [('annually', 0.329458878927762),\n", " ('ništa', 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('pretty', 0.028267451628896725)],\n", " 89: [('attention', 0.843935036367945),\n", " ('bcos', 0.05718547642432528),\n", " ('grunt', 0.05490768918278214),\n", " ('creatively', 0.04939794557128203),\n", " ('salinda', 0.04322767197221048),\n", " ('operated', 0.03721885675228483),\n", " ('pleasing', 0.03527461479149289),\n", " ('smallest', 0.03521825865881949),\n", " ('christmas', 0.031223980209004982),\n", " ('written', 0.031035183575686798)],\n", " 90: [('excellent', 0.6612472484256475),\n", " ('service', 0.6087910750117775),\n", " ('round', 0.034946593637527654),\n", " ('provided', 0.017475839151079767),\n", " ('felt', 0.01723352004581543),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05)],\n", " 91: [('staft', 0.1016173365286821),\n", " ('helpfull', 0.04363898883048405),\n", " ('traditional', 0.031441111205715175),\n", " ('furniture', 0.02749043937053419),\n", " ('autumn', 0.026601519070935886),\n", " ('program', 0.024738808910258737),\n", " ('service', 0.024626862285864276),\n", " ('specious', 0.024548200755082083),\n", " ('lana', 0.024226767665618962),\n", " ('professional', 0.023960569105834394)],\n", " 92: [('rien', 3.2458073198132764),\n", " ('tout', 3.1245426506861724),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05)],\n", " 93: [('halal', 0.7798553950927957),\n", " ('malaysian', 0.0723469914052686),\n", " ('mahal', 0.04643025498591554),\n", " ('taj', 0.04643025498591554),\n", " ('restaurants', 0.04425630323929235),\n", " ('easy', 0.028355805026104),\n", " ('patronage', 0.02582456770692671),\n", " ('ust', 0.02582456770692671),\n", " ('road', 0.02491431851267433),\n", " ('restoran', 0.024178182431187415)],\n", " 94: [('putt', 0.04428800368950314),\n", " ('playgrounds', 0.04334596955674083),\n", " ('slides', 0.035872175217483065),\n", " ('oath', 0.028093392776785905),\n", " ('playmat', 0.028093392776785905),\n", " ('playhouse', 0.028093392776785905),\n", " ('areas', 0.02547371923926242),\n", " ('cordoned', 0.024511383929810664),\n", " ('small', 0.022549387960068383),\n", " ('bowling', 0.02241620220409382)],\n", " 95: [('real', 0.10616783201604776),\n", " ('gems', 0.10443314343332263),\n", " ('nugget', 0.07952355315257735),\n", " ('shy', 0.05739381728526461),\n", " ('seek', 0.05574415038691407),\n", " ('industry', 0.05121920625064184),\n", " ('heartbeat', 0.04897539094742085),\n", " ('opinion', 0.03807380634528432),\n", " ('price', 0.011313049198121808),\n", " ('lovely', 0.00951077792113555)],\n", " 96: [('big', 0.7324287258038149),\n", " ('room', 0.17985990756578452),\n", " ('huge', 0.024181654213717898),\n", " ('space', 0.023018360582918618),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05)],\n", " 97: [('difficult', 0.49935960562295134),\n", " ('someting', 0.08270340185638943),\n", " ('timer', 0.06533428610030789),\n", " ('pros', 0.058900210123952926),\n", " ('wanted', 0.051297162145702485),\n", " ('figure', 0.051245955319718546),\n", " ('instructions', 0.04696927550519389),\n", " ('adventure', 0.0459918413481378),\n", " ('tucked', 0.04201200643293406),\n", " ('knew', 0.03826881630278259)],\n", " 98: [('dora', 1.122185273375143),\n", " ('cruise', 0.4702767348180315),\n", " ('glad', 0.050895751266919746),\n", " ('bruce', 0.043853230185069085),\n", " ('regulated', 0.02942963196220825),\n", " ('1night', 0.027637793963084818),\n", " ('exclusive', 0.023398955292829232),\n", " ('heartbeat', 0.02199596505708726),\n", " ('efforts', 0.020743824069819138),\n", " ('boyfriend', 0.019900230693528715)]}}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_keyword_weights = topic_model_booking.get_topics(full=True)\n", "topic_keyword_weights" ] }, { "cell_type": "code", "execution_count": 26, "id": "380ec379", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:13:09.851202Z", "start_time": "2024-03-18T03:13:09.846770Z" }, "id": "380ec379" }, "outputs": [], "source": [ "topic_keyword_weights_path_out = os.path.join(output_subdir, 'topic_keyword_weights_'+doc_type+'_'+doc_level+'_'+doc_time+'.json')\n", "with open(topic_keyword_weights_path_out, 'w', encoding=\"utf-8\") as f:\n", " f.write(json.dumps(str(topic_keyword_weights),indent=4, ensure_ascii=False))" ] }, { "cell_type": "code", "execution_count": 67, "id": "be9c7e1b-a38b-4123-aecd-68689db2583a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.06739237208619099" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Coherence score\n", "import gensim.corpora as corpora\n", "from gensim.models.coherencemodel import CoherenceModel\n", "\n", "\n", "def check_valid(list_topics):\n", " count = 0\n", " for topic in list_topics:\n", " if topic[0] != '':\n", " count += 1\n", " \n", " return True if count > 2 else False\n", "\n", "cleaned_docs = topic_model_booking._preprocess_text(df_documents_booking)\n", "vectorizer = topic_model_booking.vectorizer_model\n", "analyzer = vectorizer.build_analyzer()\n", "tokens = [analyzer(doc) for doc in cleaned_docs]\n", "dictionary = corpora.Dictionary(tokens)\n", "corpus = [dictionary.doc2bow(token) for token in tokens]\n", "topics = topic_model_booking.get_topics()\n", "# topics.pop(-1, None)\n", "\n", "# topic_words = [\n", "# [word for word, _ in topic_model_booking.get_topic(topic) if word != \"\"] for topic in topics\n", "# ]\n", "\n", "topic_words = [\n", " [word for word, _ in topic_model_booking.get_topic(topic) if word != \"\"] for topic in topics if check_valid(topic_model_booking.get_topic(topic))\n", " ]\n", "\n", "\n", "\n", " # Evaluate\n", "coherence_model = CoherenceModel(topics=topic_words, \n", " texts=tokens, \n", " corpus=corpus,\n", " dictionary=dictionary, \n", " coherence='c_npmi')\n", "coherence = coherence_model.get_coherence()\n", "coherence_path = os.path.join(output_subdir, 'coherence_score_booking'+'.txt')\n", "with open(coherence_path, 'w', encoding=\"utf-8\") as f:\n", " f.write(f'Coherence score: {coherence}')" ] }, { "cell_type": "code", "execution_count": 27, "id": "2356c489", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:13:34.325401Z", "start_time": "2024-03-18T03:13:33.968688Z" }, "id": "2356c489" }, "outputs": [], "source": [ "df_topics_booking = topic_model_booking.get_document_info(df_documents_booking)" ] }, { "cell_type": "code", "execution_count": 28, "id": "089368fc", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:13:37.654167Z", "start_time": "2024-03-18T03:13:37.512366Z" }, "id": "089368fc" }, "outputs": [], "source": [ "df_doc_out = pd.concat([df_topics_booking,df_doc_out_booking.loc[:,\"review_id\":]],axis=1)" ] }, { "cell_type": "code", "execution_count": 29, "id": "457e6c92", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:13:40.966038Z", "start_time": "2024-03-18T03:13:40.953982Z" }, "id": "457e6c92", "outputId": "6cb80351-2e4b-4380-b9af-302da5930724" }, "outputs": [ { "data": { "text/html": [ "
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DocumentTopicNameRepresentationRepresentative_DocsTop_n_wordsProbabilityRepresentative_documentreview_iddatemonthlyquarterlyyearly
0The staff were very kind and helpful.-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False02022-06-302022-062022Q22022
1The dog was also a sweetheart.3636_ants_cutest_pets_mosquitos[ants, cutest, pets, mosquitos, cockroach, run...[and there’s cute dogs!, The cute dogs!, that ...ants - cutest - pets - mosquitos - cockroach -...0.882780False02022-06-302022-062022Q22022
2The water pressure in the shower was weak.-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False02022-06-302022-062022Q22022
3The bed mat was also hard, so it was hard for ...-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False02022-06-302022-062022Q22022
4Location is superb, indoor decor with live pla...-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False12023-07-312023-072023Q32023
..........................................
919235No6666_nope_nah_th_recomend[nope, nah, th, recomend, fully, highly, price...[Nope, Nope, nope]nope - nah - th - recomend - fully - highly - ...1.000000False2430972023-05-312023-052023Q22023
919236staff was great room door allows light from ha...-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False2430982023-03-312023-032023Q12023
919237This is a quick fix that should be done.-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False2430982023-03-312023-032023Q12023
919238Payment method should be clarified as I was un...2222_early_card_bus_process[early, card, bus, process, checkout, shower, ...[staff were great from check in to check out.,...early - card - bus - process - checkout - show...0.870855False2430982023-03-312023-032023Q12023
919239com or pay direct to hotel.-1-1_staff_hotel_breakfast_helpful[staff, hotel, breakfast, helpful, beach, serv...[Lovely breakfast, close to beach and friendly...staff - hotel - breakfast - helpful - beach - ...0.000000False2430982023-03-312023-032023Q12023
\n", "

919240 rows × 13 columns

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" ], "text/plain": [ " Document Topic \\\n", "0 The staff were very kind and helpful. -1 \n", "1 The dog was also a sweetheart. 36 \n", "2 The water pressure in the shower was weak. -1 \n", "3 The bed mat was also hard, so it was hard for ... -1 \n", "4 Location is superb, indoor decor with live pla... -1 \n", "... ... ... \n", "919235 No 66 \n", "919236 staff was great room door allows light from ha... -1 \n", "919237 This is a quick fix that should be done. -1 \n", "919238 Payment method should be clarified as I was un... 22 \n", "919239 com or pay direct to hotel. -1 \n", "\n", " Name \\\n", "0 -1_staff_hotel_breakfast_helpful \n", "1 36_ants_cutest_pets_mosquitos \n", "2 -1_staff_hotel_breakfast_helpful \n", "3 -1_staff_hotel_breakfast_helpful \n", "4 -1_staff_hotel_breakfast_helpful \n", "... ... \n", "919235 66_nope_nah_th_recomend \n", "919236 -1_staff_hotel_breakfast_helpful \n", "919237 -1_staff_hotel_breakfast_helpful \n", "919238 22_early_card_bus_process \n", "919239 -1_staff_hotel_breakfast_helpful \n", "\n", " Representation \\\n", "0 [staff, hotel, breakfast, helpful, beach, serv... \n", "1 [ants, cutest, pets, mosquitos, cockroach, run... \n", "2 [staff, hotel, breakfast, helpful, beach, serv... \n", "3 [staff, hotel, breakfast, helpful, beach, serv... \n", "4 [staff, hotel, breakfast, helpful, beach, serv... \n", "... ... \n", "919235 [nope, nah, th, recomend, fully, highly, price... \n", "919236 [staff, hotel, breakfast, helpful, beach, serv... \n", "919237 [staff, hotel, breakfast, helpful, beach, serv... \n", "919238 [early, card, bus, process, checkout, shower, ... \n", "919239 [staff, hotel, breakfast, helpful, beach, serv... \n", "\n", " Representative_Docs \\\n", "0 [Lovely breakfast, close to beach and friendly... \n", "1 [and there’s cute dogs!, The cute dogs!, that ... \n", "2 [Lovely breakfast, close to beach and friendly... \n", "3 [Lovely breakfast, close to beach and friendly... \n", "4 [Lovely breakfast, close to beach and friendly... \n", "... ... \n", "919235 [Nope, Nope, nope] \n", "919236 [Lovely breakfast, close to beach and friendly... \n", "919237 [Lovely breakfast, close to beach and friendly... \n", "919238 [staff were great from check in to check out.,... \n", "919239 [Lovely breakfast, close to beach and friendly... \n", "\n", " Top_n_words Probability \\\n", "0 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "1 ants - cutest - pets - mosquitos - cockroach -... 0.882780 \n", "2 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "3 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "4 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "... ... ... \n", "919235 nope - nah - th - recomend - fully - highly - ... 1.000000 \n", "919236 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "919237 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "919238 early - card - bus - process - checkout - show... 0.870855 \n", "919239 staff - hotel - breakfast - helpful - beach - ... 0.000000 \n", "\n", " Representative_document review_id date monthly quarterly \\\n", "0 False 0 2022-06-30 2022-06 2022Q2 \n", "1 False 0 2022-06-30 2022-06 2022Q2 \n", "2 False 0 2022-06-30 2022-06 2022Q2 \n", "3 False 0 2022-06-30 2022-06 2022Q2 \n", "4 False 1 2023-07-31 2023-07 2023Q3 \n", "... ... ... ... ... ... \n", "919235 False 243097 2023-05-31 2023-05 2023Q2 \n", "919236 False 243098 2023-03-31 2023-03 2023Q1 \n", "919237 False 243098 2023-03-31 2023-03 2023Q1 \n", "919238 False 243098 2023-03-31 2023-03 2023Q1 \n", "919239 False 243098 2023-03-31 2023-03 2023Q1 \n", "\n", " yearly \n", "0 2022 \n", "1 2022 \n", "2 2022 \n", "3 2022 \n", "4 2023 \n", "... ... \n", "919235 2023 \n", "919236 2023 \n", "919237 2023 \n", "919238 2023 \n", "919239 2023 \n", "\n", "[919240 rows x 13 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_doc_out" ] }, { "cell_type": "code", "execution_count": 30, "id": "da7d05cf", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:14:27.898916Z", "start_time": "2024-03-18T03:14:20.154944Z" }, "id": "da7d05cf" }, "outputs": [], "source": [ "df_doc_out_path = os.path.join(output_subdir, 'df_documents_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "df_doc_out.to_csv(df_doc_out_path, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 31, "id": "452b07bf-486e-4158-ae56-421c83d632a6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TopicCountNameRepresentationRepresentative_Docspolaritysubjectivity
0-1529762-1_staff_hotel_breakfast_clean[staff, hotel, breakfast, clean, friendly, hel...[Friendly helpful staff, great facilities and ...0.2678080.505196
103808550_staff_clean_friendly_helpful[staff, clean, friendly, helpful, hotel, break...[Good value for money, good location for the b...0.3150890.532020
21224661_com_complain_dislike_na[com, complain, dislike, na, deal, mind, big, ...[com, com., com.]0.0274000.091549
3227802_totally_amazing_overall_recommendation[totally, amazing, overall, recommendation, de...[Highly recommend, Highly recommend., Would hi...0.1679180.507326
4325233_ratio_overall_quality_incredible[ratio, overall, quality, incredible, room, cl...[Value for money., Value to money., Value for ...0.6838030.659690
54684_rien_tout_pour_tip[rien, tout, pour, tip, , , , , , ][Tout Rien, Tout Rien, Tout Rien]0.0147060.022059
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" ], "text/plain": [ " Topic Count Name \\\n", "0 -1 529762 -1_staff_hotel_breakfast_clean \n", "1 0 380855 0_staff_clean_friendly_helpful \n", "2 1 22466 1_com_complain_dislike_na \n", "3 2 2780 2_totally_amazing_overall_recommendation \n", "4 3 2523 3_ratio_overall_quality_incredible \n", "5 4 68 4_rien_tout_pour_tip \n", "\n", " Representation \\\n", "0 [staff, hotel, breakfast, clean, friendly, hel... \n", "1 [staff, clean, friendly, helpful, hotel, break... \n", "2 [com, complain, dislike, na, deal, mind, big, ... \n", "3 [totally, amazing, overall, recommendation, de... \n", "4 [ratio, overall, quality, incredible, room, cl... \n", "5 [rien, tout, pour, tip, , , , , , ] \n", "\n", " Representative_Docs polarity subjectivity \n", "0 [Friendly helpful staff, great facilities and ... 0.267808 0.505196 \n", "1 [Good value for money, good location for the b... 0.315089 0.532020 \n", "2 [com, com., com.] 0.027400 0.091549 \n", "3 [Highly recommend, Highly recommend., Would hi... 0.167918 0.507326 \n", "4 [Value for money., Value to money., Value for ... 0.683803 0.659690 \n", "5 [Tout Rien, Tout Rien, Tout Rien] 0.014706 0.022059 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df_topic_info_metric = topic_model_booking.get_topic_info_metric(docs = df_documents_booking ,df_doc_out_booking = df_doc_out_booking)\n", "# df_topic_info_metric" ] }, { "cell_type": "code", "execution_count": 32, "id": "e1c6ba6c-104d-4d9b-a328-9ca02eae951b", "metadata": {}, "outputs": [], "source": [ "# df_topic_info_metric_path = os.path.join(output_subdir, 'df_topic_info_metric_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "# df_topic_info_metric.to_csv(df_topic_info_metric_path, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 31, "id": "f8c68a50", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:17:12.666655Z", "start_time": "2024-03-18T03:17:08.846985Z" }, "id": "f8c68a50" }, "outputs": [], "source": [ "df_doc_path = os.path.join(output_subdir, 'df_docs_booking'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "df_doc_booking.to_csv(df_doc_path, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 32, "id": "afd991b1", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:17:15.986725Z", "start_time": "2024-03-18T03:17:15.981513Z" }, "id": "afd991b1", "outputId": "b019892e-339c-4cea-e061-53c75835e60e" }, "outputs": [ { "data": { "text/plain": [ "{'calculate_probabilities': False,\n", " 'ctfidf_model': ClassTfidfTransformer(),\n", " 'embedding_model': ,\n", " 'hdbscan_model': HDBSCAN(),\n", " 'language': None,\n", " 'low_memory': True,\n", " 'min_topic_size': 10,\n", " 'n_gram_range': (1, 1),\n", " 'nr_topics': 100,\n", " 'representation_model': MaximalMarginalRelevance(diversity=0.7),\n", " 'seed_topic_list': None,\n", " 'top_n_words': 30,\n", " 'umap_model': UMAP(),\n", " 'vectorizer_model': CountVectorizer(stop_words='english'),\n", " 'verbose': True,\n", " 'zeroshot_min_similarity': 0.7,\n", " 'zeroshot_topic_list': None}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_params = topic_model_booking.get_params()\n", "model_params" ] }, { "cell_type": "code", "execution_count": 33, "id": "78a5687f", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:17:21.547042Z", "start_time": "2024-03-18T03:17:21.539579Z" }, "id": "78a5687f" }, "outputs": [], "source": [ "model_params_path_txt_out = os.path.join(output_subdir, 'model_params_booking'+doc_type+'_'+doc_level+'_'+doc_time+'.txt')\n", "with open(model_params_path_txt_out, 'w', encoding=\"utf-8\") as f:\n", " f.write(json.dumps(str(model_params),indent=4, ensure_ascii=False))" ] }, { "cell_type": "code", "execution_count": null, "id": "942ab534", "metadata": { "id": "942ab534" }, "outputs": [], "source": [ "# model_params_path_pkl_out = os.path.join(output_subdir, 'model_params_'+doc_type+'_'+doc_level+'_'+doc_time+'.pkl')\n", "# with open(model_params_path_pkl_out, 'wb') as f:\n", "# pickle.dump(model_params,f,protocol=5)" ] }, { "cell_type": "code", "execution_count": 34, "id": "ff967a02", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:17:30.144995Z", "start_time": "2024-03-18T03:17:24.600096Z" }, "id": "ff967a02", "outputId": "59c014c5-5598-4551-baed-27c0374fd0ff" }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ 0, "staff | friendly | helpful | owner | location", 14409 ], [ 1, "particular | amazing | nothingj | sucks2 | sile", 12530 ], [ 2, "hostel | hue | nights | nang | hcmc", 10950 ], [ 3, "balcony | villa | river | views | rice", 8098 ], [ 4, "location | good | central | value | unbeatable", 6633 ], [ 5, "district | ben | thanh | location | walking", 5340 ], [ 6, "breakfast | buffet | options | pool | banana", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = topic_model_booking.visualize_hierarchy(hierarchical_topics=hierarchical_topics)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 42, "id": "5fbd5f9a", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T03:19:39.691066Z", "start_time": "2024-03-18T03:19:39.672217Z" }, "id": "5fbd5f9a" }, "outputs": [], "source": [ "vis_save_dir = os.path.join(output_subdir, 'bertopic_hierarchy_vis_booking'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", "fig.write_html(vis_save_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "1ec932c0", "metadata": { "id": "1ec932c0" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "e0c36697", "metadata": { "id": "e0c36697" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "bdb47c7e", "metadata": { "ExecuteTime": { "end_time": "2024-03-18T05:24:03.788912Z", "start_time": "2024-03-18T05:23:08.581079Z" }, "id": "bdb47c7e", "outputId": "ad9c3b4c-aaf2-44ef-95d9-112f8dea16eb", "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "4it [00:17, 4.26s/it]\n" ] } ], "source": [ "for key in timestamps_dict_booking.keys():\n", " topics_over_time = topic_model_booking.topics_over_time(df_documents_booking, timestamps_dict_booking[key])\n", " fig = topic_model_booking.visualize_topics_over_time(topics_over_time, top_n_topics=10, title=f\"Topics over time following {key}\")\n", " fig.show()\n", " vis_save_dir = os.path.join(output_subdir, 'bertopic_dtm_vis_booking'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", " fig.write_html(vis_save_dir)\n", "\n", " topic_dtm_path_out = os.path.join(output_subdir, 'topics_dtm_booking'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", " topics_over_time.to_csv(topic_dtm_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "51f74ac6", "metadata": { "id": "51f74ac6" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 44, "id": "3adcc5d7", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T11:46:41.110381Z", "start_time": "2024-03-12T11:41:50.270316Z" }, "id": "3adcc5d7", "outputId": "59a75ef9-b780-4a32-9558-eaed45a42d5d", "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "4it [00:07, 1.75s/it]\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "text", "hovertext": [ "Topic 0
Words: staff, clean, helpful, nice, view", "Topic 0
Words: staff, friendly, clean, helpful, view", "Topic 0
Words: staff, friendly, helpful, clean, hotel", "Topic 0
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 1
Words: nope, becarefull, legends, confident, jolie", "Topic 1
Words: nope, becarefull, jolie, tien, tam", "Topic 1
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Words: recommend, handwriting, marry, deffo, massively", "Topic 2
Words: course, recommenden, handwriting, marry, minute", "Topic 2
Words: handwriting, deffo, happily, isolation, map", "Topic 2
Words: recommenden, marry, minute, defiantly, prepare" ], "marker": { "color": "#009E73" }, "mode": "lines", "name": "2_recommend_handwriting_marry_deffo", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 38, 51, 669, 1144 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 3
Words: value, complaints, really, overrall, fpr", "Topic 3
Words: litteraly, complains, quality, major, overrall", "Topic 3
Words: value, complaints, great, fpr, unsatisfied", "Topic 3
Words: price, complains, overrall, quality, thunderstorm" ], "marker": { "color": "#F0E442" }, "mode": "lines", "name": "3_value_complaints_great_overrall", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 31, 44, 534, 920 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 4
Words: com, policies, miles, wrong, app", "Topic 4
Words: com, hermes, dishonest, reality, toiletries", "Topic 4
Words: com, rating, depute, reality, messaging", "Topic 4
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Words: bed, queen, aid, procedure, nonetheless", "Topic 6
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Words: stay, biggy, recommend, wouldn, fine", "Topic 7
Words: stay, biggy, recommend, wouldn, fine", "Topic 7
Words: biggy, visit, overall, recommend, doubt", "Topic 7
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Words: friendly, staff, helpful, hotel, balcony", "Topic 0
Words: location, staff, clean, helpful, stay", "Topic 0
Words: staff, friendly, clean, helpful, hotel", "Topic 0
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Words: staff, friendly, clean, helpful, balcony", "Topic 0
Words: staff, friendly, helpful, hotel, place", "Topic 0
Words: friendly, helpful, view, hotel, breakfast", "Topic 0
Words: location, staff, helpful, nice, hostel", "Topic 0
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Words: nope, becarefull, legends, confident, jolie", "Topic 1
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 1
Words: nope, becarefull, legends, confident, jolie", "Topic 1
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Words: nope, becarefull, legends, confident, jolie", "Topic 1
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Words: nope, manage, comfortable, legends, jolie", "Topic 1
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Words: nope, tien, tam, becarefull, legends", "Topic 1
Words: attention, really, whaaaaaa, becarefull, legends", "Topic 1
Words: nope, nancy, course, becarefull, legends", "Topic 1
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Words: recommend, handwriting, marry, deffo, massively", "Topic 2
Words: course, recommenden, handwriting, marry, minute", "Topic 2
Words: recommend, handwriting, marry, deffo, massively", "Topic 2
Words: recommenden, handwriting, marry, minute, defiantly", "Topic 2
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Words: recommended, deffo, warmly, handwriting, marry", "Topic 2
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Words: nul, complaints, støj, breafirst, netong", "Topic 5
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Words: loving, complaints, comfortable, gar, nakakaintindi", "Topic 5
Words: deepl, naboerne, støj, pga, a1", "Topic 5
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Words: recommend, handwriting, marry, deffo, massively", "Topic 2
Words: recommend, handwriting, marry, deffo, massively", "Topic 2
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Words: tips, felt, home, friendly, breakfast", "Topic 2
Words: anytime, recommenden, handwriting, marry, deffo", "Topic 2
Words: fabulous, recommenden, handwriting, marry, minute", "Topic 2
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Words: value, complaints, registration, mile, ones", "Topic 3
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Words: complaint, compatible, shower, comfortable, really", "Topic 3
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Words: location, staff, friendly, helpful, stay", "Topic 0
Words: staff, friendly, clean, helpful, hotel", "Topic 0
Words: location, staff, helpful, nice, hostel", "Topic 0
Words: location, staff, helpful, clean, breakfast" ], "marker": { "color": "#E69F00" }, "mode": "lines", "name": "0_location_staff_helpful_nice", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 2136, 3052, 37239, 63154 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 1
Words: reminder, dislikes, complaint, change, property", "Topic 1
Words: really, negative, property, complaints, saying", "Topic 1
Words: particular, property, think, don, complain", "Topic 1
Words: dislike, property, think, great, wrong" ], "marker": { "color": "#56B4E9" }, "mode": "lines", "name": "1_dislike_particular_property_say", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 252, 407, 5043, 8854 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 2
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 2
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 2
Words: jolie, tien, manage, tam, nooooothing", "Topic 2
Words: nooooothing, legends, confident, nancy, course" ], "marker": { "color": "#009E73" }, "mode": "lines", "name": "2_nooooothing_becarefull_legends_confide...", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 46, 91, 1164, 1734 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 3
Words: matches, apart, recommend, exceeded, thing", "Topic 3
Words: ok, complaints, expectation, honest, fabulous", "Topic 3
Words: expectations, thing, complain, apart, rest", "Topic 3
Words: liked, exceeded, needed, thing, price" ], "marker": { "color": "#F0E442" }, "mode": "lines", "name": "3_liked_expectations_exceeded_thing", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 38, 79, 900, 1557 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 4
Words: recommenden, handwriting, marry, defiantly, minute", "Topic 4
Words: course, recommenden, handwriting, marry, defiantly", "Topic 4
Words: handwriting, warmly, impressive, modern, owner", "Topic 4
Words: recommenden, marry, defiantly, minute, prepare" ], "marker": { "color": "#D55E00" }, "mode": "lines", "name": "4_recommenden_handwriting_marry_defiantl...", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 38, 51, 669, 1144 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 5
Words: thank, hospitality, notch, guys, gesture", "Topic 5
Words: thank, hospitality, guys, sending, forgotten", "Topic 5
Words: thank, hospitality, mumbai, biradar, basavaraj", "Topic 5
Words: thank, hospitality, gesture, lot, support" ], "marker": { "color": "#0072B2" }, "mode": "lines", "name": "5_thank_hospitality_guys_gesture", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 34, 51, 588, 1034 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 6
Words: overrall, fpr, compatible, thunderstorm, thrown", "Topic 6
Words: overrall, fpr, compatible, thunderstorm, thrown", "Topic 6
Words: fpr, ratio, line, discount, speaking", "Topic 6
Words: overrall, thunderstorm, compatible, super, extras" ], "marker": { "color": "#CC79A7" }, "mode": "lines", "name": "6_overrall_fpr_thunderstorm_compatible", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 17, 28, 402, 689 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 7
Words: nil, complaints, everythign, gar, breafirst", "Topic 7
Words: nil, complaints, everythign, gar, breafirst", "Topic 7
Words: nil, breafirst, action, complaints, poor", "Topic 7
Words: nil, complaints, everythign, gar, a1" ], "marker": { "color": "#E69F00" }, "mode": "lines", "name": "7_nil_complaints_gar_breafirst", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 15, 19, 358, 655 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 8
Words: , , , , ", "Topic 8
Words: , , , , ", "Topic 8
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Words: com, policies, miles, wrong, app", "Topic 9
Words: com, hermes, dishonest, reality, toiletries", "Topic 9
Words: com, rating, depute, reality, messaging", "Topic 9
Words: com, misleading, price, scammer, lying" ], "marker": { "color": "#009E73" }, "mode": "lines", "name": "9_com_misleading_reality_price", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 20, 22, 220, 355 ] } ], "layout": { "height": 450, "hoverlabel": { "bgcolor": "white", "font": { "family": "Rockwell", "size": 16 } }, "legend": { "title": { "text": "Global Topic Representation" } }, "template": { "data": { "bar": [ { "error_x": { "color": "rgb(36,36,36)" }, "error_y": { "color": "rgb(36,36,36)" }, "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "white", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "rgb(36,36,36)", "gridcolor": "white", "linecolor": "white", 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Words: com, theft, misleading, rating, missed", "Topic 9
Words: com, messaging, misleading, sim, forget", "Topic 9
Words: com, app, misleading, advertises, jewel", "Topic 9
Words: com, delete, administration, argument, deceiving", "Topic 9
Words: com, scammer, lying, quoted, confirmation", "Topic 9
Words: com, queen, disappointing, reviews, support", "Topic 9
Words: com, sage, messaging, apologetic, rating", "Topic 9
Words: com, reimbursed, reflecting, unreliable, advertisement", "Topic 9
Words: credible, webpage, messages, onsite, charged", "Topic 9
Words: com, ad, guess, fix, misleading", "Topic 9
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Words: staff, friendly, clean, helpful, view", "Topic 0
Words: staff, friendly, clean, helpful, hotel", "Topic 0
Words: location, staff, helpful, nice, hostel", "Topic 0
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Words: change, particular, love, amazing, finish", "Topic 1
Words: saying, trees, deal, elevator, amazing", "Topic 1
Words: sucks2, atall, niente, gro, fabulous", "Topic 1
Words: perfect, notable, error404, hotzl, nothingvto" ], "marker": { "color": "#56B4E9" }, "mode": "lines", "name": "1_perfect_notable_nada_things", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 241, 398, 4832, 8493 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 2
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 2
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 2
Words: jolie, tien, manage, tam, nooooothing", "Topic 2
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Words: matches, apart, recommend, exceeded, thing", "Topic 3
Words: complaints, honest, reasonable, recommend, things", "Topic 3
Words: good, thing, apart, asked, surpassed", "Topic 3
Words: liked, exceeded, needed, thing, apart" ], "marker": { "color": "#F0E442" }, "mode": "lines", "name": "3_liked_exceeded_thing_needed", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 38, 79, 900, 1557 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 4
Words: thank, hospitality, notch, guys, gesture", "Topic 4
Words: thank, hospitality, guys, sending, forgotten", "Topic 4
Words: thank, hospitality, biradar, basavaraj, mumbai", "Topic 4
Words: thank, hospitality, lot, support, efforts" ], "marker": { "color": "#D55E00" }, "mode": "lines", "name": "4_thank_hospitality_guys_gesture", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 34, 51, 588, 1034 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 5
Words: 00am, lasted, 20min, 10, points", "Topic 5
Words: 70, 10, 3m, imagine, arrived", "Topic 5
Words: rate, summarize, 45, singles, minutes", "Topic 5
Words: 11, points, arrived, 10pm, rate" ], "marker": { "color": "#0072B2" }, "mode": "lines", "name": "5_11_points_arrived_10pm", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 39, 34, 526, 812 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 6
Words: overrall, fpr, compatible, thunderstorm, super", "Topic 6
Words: overrall, fpr, compatible, thunderstorm, super", "Topic 6
Words: fpr, ratio, discount, overrall, thunderstorm", "Topic 6
Words: overrall, compatible, thunderstorm, extras, super" ], "marker": { "color": "#CC79A7" }, "mode": "lines", "name": "6_overrall_fpr_thunderstorm_compatible", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 17, 28, 402, 689 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 7
Words: nil, complaints, everythign, gar, breafirst", "Topic 7
Words: nil, complaints, everythign, gar, breafirst", "Topic 7
Words: nil, breafirst, action, complaints, poor", "Topic 7
Words: nil, complaints, everythign, gar, a1" ], "marker": { "color": "#E69F00" }, "mode": "lines", "name": "7_nil_complaints_gar_breafirst", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 15, 19, 358, 655 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 8
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 8
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 8
Words: deffo, isolation, warmly, modern, recommenden", "Topic 8
Words: recommenden, defiantly, minute, anytime, tips" ], "marker": { "color": "#56B4E9" }, "mode": "lines", "name": "8_recommenden_deffo_defiantly_minute", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 14, 21, 338, 604 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, course, handwriting, marry, impressive", "Topic 9
Words: recommend, handwriting, impressive, map, truly", "Topic 9
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Words: overrall, fpr, compatible, thunderstorm, super", "Topic 6
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Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, handwriting, impressive, map, owner", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
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Words: recommend, truly, handwriting, marry, impressive", "Topic 9
Words: recommend, truly, handwriting, marry, impressive", "Topic 9
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Words: recommend, truly, handwriting, marry, impressive", "Topic 9
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Words: recommend, truly, handwriting, marry, impressive", "Topic 9
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Words: recommend, truly, handwriting, marry, impressive", "Topic 9
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Words: friendly, staff, clean, helpful, breakfast", "Topic 0
Words: friendly, staff, clean, helpful, view", "Topic 0
Words: staff, helpful, hostel, nice, location", "Topic 0
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Words: change, particular, amazing, nada, nothingmr", "Topic 1
Words: potential, saying, trees, deal, 100", "Topic 1
Words: great, sucks2, atall, niente, nada", "Topic 1
Words: perfect, notable, hotzl, sile, error404" ], "marker": { "color": "#56B4E9" }, "mode": "lines", "name": "1_particular_amazing_things_hotzl", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 241, 398, 4832, 8493 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 2
Words: location, kiem, tam, hoan, coc", "Topic 2
Words: lake, kiem, hoan, tam, great", "Topic 2
Words: tam, coc, great, lake, kiem", "Topic 2
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 3
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 3
Words: jolie, tien, manage, tam, nooooothing", "Topic 3
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Words: matches, apart, recommend, exceeded, thing", "Topic 4
Words: ok, complaints, expectation, honest, fabulous", "Topic 4
Words: perfect, expectations, thing, complain, apart", "Topic 4
Words: liked, exceeded, needed, thing, apart" ], "marker": { "color": "#D55E00" }, "mode": "lines", "name": "4_recommend_exceeded_thing_apart", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 38, 78, 880, 1527 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 5
Words: receptionists, helpful, friendly, unhelpful, attitude", "Topic 5
Words: receptionists, speak, attitude, nice, desk", "Topic 5
Words: staff, receptionists, helpful, spoke, nice", "Topic 5
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Words: 00am, lasted, 20min, 10, quite", "Topic 6
Words: 70, 10, 3m, 45pm, imagine", "Topic 6
Words: 10, 00am, arrived, summarize, 70", "Topic 6
Words: 11, points, arrived, 10pm, 000" ], "marker": { "color": "#CC79A7" }, "mode": "lines", "name": "6_11_points_arrived_10pm", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 39, 34, 526, 812 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 7
Words: thank, hospitality, notch, guys, gesture", "Topic 7
Words: thank, hospitality, guys, sending, id", "Topic 7
Words: thank, hospitality, biradar, basavaraj, mumbai", "Topic 7
Words: thank, hospitality, forever, support, efforts" ], "marker": { "color": "#E69F00" }, "mode": "lines", "name": "7_thank_hospitality_forever_lot", "type": "scatter", "x": [ "2020-01-01T00:00:00", "2021-01-01T00:00:00", "2022-01-01T00:00:00", "2023-01-01T00:00:00" ], "y": [ 27, 42, 486, 840 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: price, overrall, fpr, compatible, thunderstorm", "Topic 8
Words: fpr, ratio, line, discount, speaking", "Topic 8
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Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: deffo, isolation, warmly, modern, recommenden", "Topic 9
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Words: thank, hospitality, york, joe, gesture", "Topic 7
Words: thank, hospitality, guys, efforts, marko", "Topic 7
Words: thank, guys, hospitality, lim, wishes" ], "marker": { "color": "#E69F00" }, "mode": "lines", "name": "7_thank_hospitality_forever_lot", "type": "scatter", "x": [ "2020-08-31T00:00:00", "2020-09-30T00:00:00", "2020-10-31T00:00:00", "2020-11-30T00:00:00", "2020-12-31T00:00:00", "2021-01-31T00:00:00", "2021-02-28T00:00:00", "2021-03-31T00:00:00", "2021-04-30T00:00:00", "2021-05-31T00:00:00", "2021-07-31T00:00:00", "2021-10-31T00:00:00", "2021-11-30T00:00:00", "2021-12-31T00:00:00", "2022-01-31T00:00:00", "2022-02-28T00:00:00", "2022-03-31T00:00:00", "2022-04-30T00:00:00", "2022-05-31T00:00:00", "2022-06-30T00:00:00", "2022-07-31T00:00:00", "2022-08-31T00:00:00", "2022-09-30T00:00:00", "2022-10-31T00:00:00", "2022-11-30T00:00:00", "2022-12-31T00:00:00", "2023-01-31T00:00:00", "2023-02-28T00:00:00", "2023-03-31T00:00:00", "2023-04-30T00:00:00", "2023-05-31T00:00:00", "2023-06-30T00:00:00", "2023-07-31T00:00:00", "2023-08-31T00:00:00" ], "y": [ 1, 4, 5, 8, 9, 5, 3, 9, 8, 8, 4, 1, 2, 2, 3, 8, 7, 25, 18, 29, 37, 43, 61, 60, 93, 102, 89, 109, 124, 117, 115, 92, 102, 92 ] }, { "hoverinfo": "text", "hovertext": [ "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: price, overrall, fpr, compatible, thunderstorm", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: value, overrall, fpr, compatible, thunderstorm", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: 10, overrall, fpr, compatible, thunderstorm", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: discount, overrall, fpr, compatible, thunderstorm", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: ratio, speaking, overrall, fpr, thunderstorm", "Topic 8
Words: overrall, fpr, compatible, thunderstorm, extraordinary", "Topic 8
Words: fpr, price, thunderstorm, compatible, thrown", "Topic 8
Words: total, offers, overrall, fpr, compatible", "Topic 8
Words: comfortable, overrall, fpr, thunderstorm, compatible", "Topic 8
Words: super, overrall, fpr, thunderstorm, compatible", "Topic 8
Words: mile, fpr, staff, thunderstorm, compatible", "Topic 8
Words: overrall, thrown, extras, close, bed", "Topic 8
Words: thunderstorm, quiet, overrall, fpr, quality", "Topic 8
Words: compatible, delightful, shower, overrall, fpr", "Topic 8
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Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommend, deffo, massively, minute, isolation", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommend, deffo, massively, minute, isolation", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: isolation, modern, hotel, staff, recommenden", "Topic 9
Words: deffo, warmly, recommenden, defiantly, minute", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: recommenden, defiantly, minute, isolation, warmly", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: defiantly, recommenden, deffo, minute, isolation", "Topic 9
Words: recommenden, deffo, defiantly, minute, isolation", "Topic 9
Words: minute, recommenden, defiantly, isolation, warmly", "Topic 9
Words: prepare, felt, friendly, breakfast, staff", "Topic 9
Words: anytime, recommenden, defiantly, minute, isolation", "Topic 9
Words: fabulous, recommenden, defiantly, minute, isolation", "Topic 9
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Words: friendly, staff, helpful, view, clean", "Topic 0
Words: friendly, staff, helpful, clean, balcony", "Topic 0
Words: staff, helpful, hostel, nice, location", "Topic 0
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Words: change, particular, love, amazing, nothingj", "Topic 1
Words: potential, saying, trees, deal, elevator", "Topic 1
Words: great, sucks2, atall, niente, nada", "Topic 1
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Words: buffet, excellent, options, variety, poor", "Topic 2
Words: buffet, options, limited, menu, beach", "Topic 2
Words: buffet, great, vegetarian, selection, halal", "Topic 2
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Words: location, rôm, price, convenience, ambience", "Topic 3
Words: location, great, espcecially, takimg, embassy", "Topic 3
Words: location, central, price, unbeatable, nice", "Topic 3
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: jolie, tien, manage, tam, nooooothing", "Topic 4
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Words: matches, apart, recommend, exceeded, thing", "Topic 5
Words: ok, complaints, honest, considering, reasonable", "Topic 5
Words: perfect, expectations, thing, complain, apart", "Topic 5
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Words: kiem, hoan, tam, coc, lake", "Topic 6
Words: kiem, hoan, lake, tam, coc", "Topic 6
Words: tam, coc, lake, kiem, hoan", "Topic 6
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Words: receptionists, staff, helpful, friendly, unhelpful", "Topic 7
Words: receptionists, speak, attitude, desk, nice", "Topic 7
Words: receptionists, staff, spoke, helpful, barrier", "Topic 7
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Words: service, cleanliness, maintained, convertable, wellcome", "Topic 8
Words: quiet, comfortable, super, cleanliness, squeaky", "Topic 8
Words: new, quiet, maintained, cleanliness, nice", "Topic 8
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Words: thank, hospitality, notch, guys, forever", "Topic 9
Words: thank, hospitality, guys, sending, id", "Topic 9
Words: thank, mumbai, biradar, basavaraj, kidness", "Topic 9
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Words: friendly, balcony, villa, tau, vung", "Topic 0
Words: friendly, staff, helpful, clean, hotel", "Topic 0
Words: friendly, staff, helpful, clean, balcony", "Topic 0
Words: friendly, staff, view, balcony, hostel", "Topic 0
Words: friendly, balcony, staff, rose, petal", "Topic 0
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Words: friendly, helpful, balcony, hotel, staffs", "Topic 0
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Words: trees, soon, amazing, fine, sile", "Topic 1
Words: deal, 100, elevator, experience, amazing", "Topic 1
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Words: hugo, eco, loads, opposite, thing", "Topic 1
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Words: particular, everythtng, atall, niente, nada", "Topic 1
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Words: buffet, limited, choices, nice, beach", "Topic 2
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Words: buffet, options, meals, tasty, quality", "Topic 2
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Words: location, central, convenience, remote, price", "Topic 3
Words: location, excellent, foremost, central, unbeatable", "Topic 3
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Words: location, excellent, central, unbeatable, vn", "Topic 3
Words: location, central, price, lanmdmarks, btgreat", "Topic 3
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Words: location, price, unbeatable, convenience, eastaurants", "Topic 3
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Words: becarefull, jolie, nooooothing, yay, legends", "Topic 4
Words: tien, tam, nooooothing, becarefull, legends", "Topic 4
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Words: perfect, matches, apart, rest, expectations", "Topic 5
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Words: complaints, visit, liked, expectations, exceeded", "Topic 5
Words: things, perfect, exceeded, apart, rest", "Topic 5
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Words: apart, alles, tip, exactly, complain", "Topic 5
Words: perfect, expectations, thing, fantastic, surpassed", "Topic 5
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Words: liked, exceeded, needed, thing, wonderful", "Topic 5
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Words: tam, coc, kiem, lake, dinh", "Topic 6
Words: kiem, hoan, tam, coc, lake", "Topic 6
Words: kiem, hoan, lake, tam, huc", "Topic 6
Words: coc, kiem, hoan, beach, close", "Topic 6
Words: kiem, hoan, restaurants, beach, close", "Topic 6
Words: kiem, hoan, tam, coc, beach", "Topic 6
Words: kiem, hoan, tam, coc, koi", "Topic 6
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Words: lenient, speak, unprofessional, helpful, smart", "Topic 7
Words: english, receptionists, helpful, friendly, unhelpful", "Topic 7
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Words: convertable, superior, safety, looks, cheap", "Topic 8
Words: quiet, service, cleanliness, wellcome, marriage", "Topic 8
Words: new, quiet, place, squeaky, cleanliness", "Topic 8
Words: quiet, cleanned, serviced, exceptionally, basic", "Topic 8
Words: quiet, nice, location, new, maintained", "Topic 8
Words: new, quiet, sparkly, comfortable, bucks", "Topic 8
Words: cleanliness, maintained, super, location, service", "Topic 8
Words: super, cleanliness, fancy, service, location", "Topic 8
Words: new, quiet, maintained, cleanliness, location", "Topic 8
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Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, notch, guys, forever", "Topic 9
Words: thank, id, guys, forgotten, hospitality", "Topic 9
Words: thank, treating, hospitality, guys, gesture", "Topic 9
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Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, weekend, guys, forever", "Topic 9
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Words: thank, hospitality, guys, youu, kidness", "Topic 9
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Words: thank, hospitality, york, joe, forever", "Topic 9
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Words: location, price, good, central, convenience", "Topic 3
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Words: location, rôm, ambience, idea, central", "Topic 3
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Words: excellent, central, convenient, price, thatit", "Topic 3
Words: location, excellent, central, convenience, price", "Topic 3
Words: location, price, convenience, nice, second0hand", "Topic 3
Words: excellent, central, imstaff, hiden, citidel", "Topic 3
Words: location, convenient, price, excelente, exelent", "Topic 3
Words: location, central, value, convenience, behaved", "Topic 3
Words: price, unbeatable, convenience, stadium, brekkie", "Topic 3
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
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Words: manage, nooooothing, legends, confident, jolie", "Topic 4
Words: jolie, nooooothing, becarefull, yay, legends", "Topic 4
Words: becarefull, yay, legends, confident, jolie", "Topic 4
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: tien, tam, nooooothing, becarefull, legends", "Topic 4
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Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: course, nooooothing, becarefull, legends, confident", "Topic 4
Words: nooooothing, becarefull, legends, confident, jolie", "Topic 4
Words: nancy, nooooothing, legends, confident, jolie", "Topic 4
Words: legends, confident, becarefull, jolie, really", "Topic 4
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Words: liked, exceeded, thing, apart, rest", "Topic 5
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Words: apart, rest, exceeded, thing, wonderful", "Topic 5
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Words: honest, meet, reasonable, price, recommend", "Topic 5
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Words: alles, tip, thing, expectations, exceeded", "Topic 5
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Words: needed, exceeded, locaton, okey, detergent", "Topic 5
Words: liked, superseded, exceeded, cot, everthing", "Topic 5
Words: suoer, highly, evrything, posted, scene", "Topic 5
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Words: distance, tam, coc, lake, kiem", "Topic 6
Words: 200m, far, hotel, tam, coc", "Topic 6
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Words: kiem, hoan, tam, coc, walk", "Topic 6
Words: kiem, hoan, tam, coc, 4p", "Topic 6
Words: tam, coc, lake, huong, step", "Topic 6
Words: kiem, lake, tam, shipyard, huc", "Topic 6
Words: beach, tam, coc, square, restaurants", "Topic 6
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Words: convertable, superior, safety, looks, cheap", "Topic 8
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Words: wellcome, equipted, marriage, cleanliness, safety", "Topic 8
Words: new, quiet, cleaniness, comfortable, shape", "Topic 8
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Words: squeaky, comfortable, new, looks, services", "Topic 8
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Words: quiet, serviced, basic, extremely, thank", "Topic 8
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Words: receptionists, helpful, rude, speaking, translate", "Topic 7
Words: receptionists, helpful, rude, barrier, attitude", "Topic 7
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Words: convertable, superior, safety, looks, cheap", "Topic 8
Words: new, looks, service, windows, comfortable", "Topic 8
Words: wellcome, equipted, marriage, cleanliness, safety", "Topic 8
Words: new, quiet, cleaniness, comfortable, shape", "Topic 8
Words: new, quiet, cleanliness, extremely, time", "Topic 8
Words: squeaky, comfortable, new, looks, services", "Topic 8
Words: quiet, cozy, convenient, super, cleanliness", "Topic 8
Words: exceptionally, new, pleasant, comfortable, maintained", "Topic 8
Words: quiet, serviced, basic, extremely, thank", "Topic 8
Words: quiet, new, maintained, cleanliness, location", "Topic 8
Words: location, new, quiet, maintained, cleanliness", "Topic 8
Words: quiet, nice, new, maintained, cleanliness", "Topic 8
Words: sparkly, bucks, notch, exceptional, comfortable", "Topic 8
Words: elegant, quiet, comfortable, new, location", "Topic 8
Words: new, neat, total, quiet, cleaning", "Topic 8
Words: quiet, new, morden, nice, equiped", "Topic 8
Words: new, cleanliness, comfortable, conftable, freak", "Topic 8
Words: new, locarion, nice, organized, remote", "Topic 8
Words: value, quiet, maintained, cleanliness, loundry", "Topic 8
Words: new, comfortable, sustainable, stopover, light", "Topic 8
Words: comfortable, super, harmonized, doi, owner", "Topic 8
Words: maintained, service, cleanliness, location, vintage", "Topic 8
Words: maintained, super, cleanliness, location, prepaid", "Topic 8
Words: maintained, cleanliness, brand, location, exceptionally", "Topic 8
Words: new, cleanliness, comfy, exceptionally, restaurans", "Topic 8
Words: quiet, cleanliness, maintained, location, confortable", "Topic 8
Words: new, maintained, quiet, cleanliness, looks", "Topic 8
Words: new, quiet, maintained, cleanliness, money", "Topic 8
Words: new, comfortable, quiet, maintained, neat", "Topic 8
Words: new, cleanliness, brand, location, exceptionally", "Topic 8
Words: new, comfortable, quiet, maintained, cleanliness", "Topic 8
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Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, forever, lot", "Topic 9
Words: thank, notch, hospitality, guys, forever", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, effort, hospitality, guys, forever", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, forgotten, guys, hospitality, forever", "Topic 9
Words: thank, treating, hospitality, guys, forever", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, forever, lot", "Topic 9
Words: thank, weekend, hospitality, guys, forever", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, hospitality, guys, lot, support", "Topic 9
Words: thank, leon, hospitality, lot, support", "Topic 9
Words: thank, hospitality, guys, kindness, gesture", "Topic 9
Words: thank, gesture, hospitality, kidness, jardin", "Topic 9
Words: thank, youu, guys, pullman, hoang", "Topic 9
Words: thank, hospitality, biradar, basavaraj, mumbai", "Topic 9
Words: thank, hospitality, guys, kha, nana", "Topic 9
Words: thank, hospitality, guys, thao, 10", "Topic 9
Words: thank, hospitality, guys, tyuet, lucky", "Topic 9
Words: thank, hospitality, guys, otis, gesture", "Topic 9
Words: thank, guys, hospitality, liu, zoni", "Topic 9
Words: thank, hospitality, doing, gesture, ibis", "Topic 9
Words: thank, guys, hospitality, support, bear", "Topic 9
Words: thank, hospitality, york, joe, gesture", "Topic 9
Words: thank, hospitality, guys, efforts, marko", "Topic 9
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for n_topics in [10,20,30,40,50]:\n", " topic_model_copy = copy.deepcopy(topic_model_booking)\n", " topic_model_copy.reduce_topics(df_documents_booking, nr_topics=n_topics)\n", " fig = topic_model_copy.visualize_topics(title=f\"Intertopic Distance Map: {n_topics} topics\")\n", " fig.show()\n", " vis_save_dir = os.path.join(output_subdir, 'bertopic_reduce_vis_booking'+str(n_topics)+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", " fig.write_html(vis_save_dir)\n", "\n", " topic_info = topic_model_copy.get_topic_info()\n", " topic_info_path_out = os.path.join(output_subdir, 'topic_reduce_info_booking'+str(n_topics)+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", " topic_info.to_csv(topic_info_path_out, encoding='utf-8')\n", "\n", " for key in timestamps_dict_booking.keys():\n", " topics_over_time_ = topic_model_copy.topics_over_time(df_documents_booking, timestamps_dict_booking[key])\n", " fig = topic_model_copy.visualize_topics_over_time(topics_over_time_, top_n_topics=10, title=f\"Topics over time following {key}\")\n", " fig.show()\n", " vis_save_dir = os.path.join(output_subdir, 'bertopic_reduce_dtm_vis_booking'+str(n_topics)+'_'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", " fig.write_html(vis_save_dir)\n", "\n", " topic_dtm_path_out = os.path.join(output_subdir, 'topics_reduce_dtm_booking'+str(n_topics)+'_'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", " topics_over_time_.to_csv(topic_dtm_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "f714f757", "metadata": { "id": "f714f757" }, "outputs": [], "source": [ "# topic_info = topic_model_copy.get_topic_info()\n", "# topic_info" ] }, { "cell_type": "code", "execution_count": null, "id": "7b11b645", "metadata": { "id": "7b11b645" }, "outputs": [], "source": [ "# topic_info_path_out = os.path.join(output_subdir, 'topic_reduce_info_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "# topic_info.to_csv(topic_info_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "f2007b2d", "metadata": { "id": "f2007b2d" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9f142543", "metadata": { "id": "9f142543" }, "outputs": [], "source": [ "# for key in timestamps_dict.keys():\n", "# topics_over_time_ = topic_model_copy.topics_over_time(df_documents, timestamps_dict[key])\n", "# fig = topic_model_copy.visualize_topics_over_time(topics_over_time_, top_n_topics=10)\n", "# fig.show()\n", "# vis_save_dir = os.path.join(output_subdir, 'bertopic_reduce_dtm_vis_'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", "# fig.write_html(vis_save_dir)\n", "\n", "# topic_dtm_path_out = os.path.join(output_subdir, 'topics_reduce_dtm_'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "# topics_over_time_.to_csv(topic_dtm_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "02a067cf", "metadata": { "id": "02a067cf" }, "outputs": [], "source": [ "# params = {\"num_topics\": [(i+1)*10 for i in range(5)], \"random_state\": 42}\n", "# params" ] }, { "cell_type": "code", "execution_count": null, "id": "edf52c49", "metadata": { "id": "edf52c49" }, "outputs": [], "source": [ "# params_name = list(params.keys())\n", "# params_name" ] }, { "cell_type": "code", "execution_count": null, "id": "334ec2b2", "metadata": { "id": "334ec2b2" }, "outputs": [], "source": [ "# new_params = list(itertools.product(params.values()))\n", "# new_params" ] }, { "cell_type": "code", "execution_count": null, "id": "b78ff9ba", "metadata": { "id": "b78ff9ba" }, "outputs": [], "source": [ "# for param_combo in new_params:\n", "# params_to_use = {param: value for param, value in zip(params_name, param_combo)}\n", "# print(params_to_use)" ] }, { "cell_type": "code", "execution_count": null, "id": "15daba81", "metadata": { "id": "15daba81" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "db262dfa", "metadata": { "id": "db262dfa" }, "outputs": [], "source": [ "# params.values()" ] }, { "cell_type": "code", "execution_count": null, "id": "af01b4bf", "metadata": { "id": "af01b4bf" }, "outputs": [], "source": [ "# {'num_topics': 10, 'random_state': 42}" ] }, { "cell_type": "code", "execution_count": null, "id": "887b0d21", "metadata": { "id": "887b0d21" }, "outputs": [], "source": [ "# {'num_topics': 20, 'random_state': 42}" ] }, { "cell_type": "code", "execution_count": null, "id": "2b45e1ae", "metadata": { "id": "2b45e1ae" }, "outputs": [], "source": [ "# {'num_topics': 30, 'random_state': 42}" ] }, { "cell_type": "code", "execution_count": null, "id": "5523bd09", "metadata": { "id": "5523bd09" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "e47b399d", "metadata": { "id": "e47b399d" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "a710c9ee21a93c7a", "metadata": {}, "source": [ "## Topic Modeling for TripAdvisor dataset\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "3c323e99bdb76376", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:10:43.469605Z", "start_time": "2024-03-12T15:10:43.458897Z" }, "collapsed": false }, "outputs": [], "source": [ "# Make new directories output of bertopic\n", "# output_subdir_name = 'bertopic_'+doc_type+'_'+doc_level+'_'+doc_time\n", "source = 'en_tripadvisor'\n", "output_subdir_name = source + '/bertopic2_non_zeroshot_30topics_'+doc_type+'_'+doc_level+'_'+doc_time\n", "output_subdir = os.path.join(output_dir, output_subdir_name)\n", "if not os.path.exists(output_subdir):\n", " os.makedirs(output_subdir)" ] }, { "cell_type": "code", "execution_count": 68, "id": "1d0c201ed29aee6e", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:12:15.261380Z", "start_time": "2024-03-12T15:11:32.432114Z" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[I] [03:10:17.773406] Unused keyword parameter: low_memory during cuML estimator initialization\n", "[I] [03:10:17.775442] Unused keyword parameter: leaf_size during cuML estimator initialization\n" ] } ], "source": [ "topic_model_tripadvisor = create_model_bertopic()" ] }, { "cell_type": "code", "execution_count": 33, "id": "0adda7e6-1de9-4f59-9289-8fad826665a2", "metadata": {}, "outputs": [], "source": [ "# output_model = os.path.abspath('/notebooks/TopicModelingRepo/outputs/2024_03_19/en_tripadvisor/bertopic2_reviews_sentence_2024_Jan_15/topics_bertopic_reviews_sentence_2024_Jan_15')" ] }, { "cell_type": "code", "execution_count": null, "id": "f30c91d6-5ca7-44bd-8228-dc0944f15043", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 35, "id": "bd9b695d-adde-4b0d-a747-708f470e2646", "metadata": {}, "outputs": [], "source": [ "# Load model is trained\n", "# embedding_model = SentenceTransformer(\"thenlper/gte-small\")\n", "# topic_model_tripadvisor = BERTopic.load(output_model, embedding_model=embedding_model)" ] }, { "cell_type": "code", "execution_count": null, "id": "1d083fb37c9cf013", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:34:07.549203Z", "start_time": "2024-03-12T15:12:50.097328Z" }, "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-03-26 03:10:20,387 - BERTopic - Embedding - Transforming documents to embeddings.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2d190c699c224a03bd1bddabcedfa45a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/103640 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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TopicCountNameRepresentationRepresentative_Docs
0-12925546-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...
101244540_hue_hotel_bay_halong[hue, hotel, bay, halong, hcmc, la, phu, siest...[We would definitely recommend this place for ...
21232791_friendly_staff_owner_reception[friendly, staff, owner, reception, desk, amaz...[The staff was friendly and helpful., Friendly...
32223942_spacious_comfortable_bathroom_dorms[spacious, comfortable, bathroom, dorms, spotl...[Rooms are clean and spacious., The rooms are ...
43211463_breakfast_buffet_included_variety[breakfast, buffet, included, variety, options...[Breakfast…, Breakfast…, No even their breakfa...
..................
95946394_small_unsurprising_tastefulness_luxory[small, unsurprising, tastefulness, luxory, sp...[Small & boutique., Small as in name, boutique...
96956195_cam_ơn_colmar_tuyet[cam, ơn, colmar, tuyet, thumbs, emerald, nhi,...[Cam on!, Cam on!, Cam on!]
97966096_say____oh_talk[say, __, oh, talk, tell, said, thing, short, ...[What can I say?, What can I say?, What can I ...
98975597_penny_dollar_99usd_worths[penny, dollar, 99usd, worths, pricey, hands, ...[Worth every penny., Worth every penny., Worth...
99985198_pay___[pay, , , , , , , , , , , , , , , , , , , , , ...[You get what you pay for., You get what you p...
\n", "

100 rows × 5 columns

\n", "" ], "text/plain": [ " Topic Count Name \\\n", "0 -1 2925546 -1_hotel_staff_breakfast_clean \n", "1 0 124454 0_hue_hotel_bay_halong \n", "2 1 23279 1_friendly_staff_owner_reception \n", "3 2 22394 2_spacious_comfortable_bathroom_dorms \n", "4 3 21146 3_breakfast_buffet_included_variety \n", ".. ... ... ... \n", "95 94 63 94_small_unsurprising_tastefulness_luxory \n", "96 95 61 95_cam_ơn_colmar_tuyet \n", "97 96 60 96_say____oh_talk \n", "98 97 55 97_penny_dollar_99usd_worths \n", "99 98 51 98_pay___ \n", "\n", " Representation \\\n", "0 [hotel, staff, breakfast, clean, friendly, sta... \n", "1 [hue, hotel, bay, halong, hcmc, la, phu, siest... \n", "2 [friendly, staff, owner, reception, desk, amaz... \n", "3 [spacious, comfortable, bathroom, dorms, spotl... \n", "4 [breakfast, buffet, included, variety, options... \n", ".. ... \n", "95 [small, unsurprising, tastefulness, luxory, sp... \n", "96 [cam, ơn, colmar, tuyet, thumbs, emerald, nhi,... \n", "97 [say, __, oh, talk, tell, said, thing, short, ... \n", "98 [penny, dollar, 99usd, worths, pricey, hands, ... \n", "99 [pay, , , , , , , , , , , , , , , , , , , , , ... \n", "\n", " Representative_Docs \n", "0 [The service was friendly and room was clean.,... \n", "1 [We would definitely recommend this place for ... \n", "2 [The staff was friendly and helpful., Friendly... \n", "3 [Rooms are clean and spacious., The rooms are ... \n", "4 [Breakfast…, Breakfast…, No even their breakfa... \n", ".. ... \n", "95 [Small & boutique., Small as in name, boutique... \n", "96 [Cam on!, Cam on!, Cam on!] \n", "97 [What can I say?, What can I say?, What can I ... \n", "98 [Worth every penny., Worth every penny., Worth... \n", "99 [You get what you pay for., You get what you p... \n", "\n", "[100 rows x 5 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_info = topic_model_tripadvisor.get_topic_info()\n", "topic_info" ] }, { "cell_type": "code", "execution_count": null, "id": "82975d8063d50f8f", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:55:57.858051Z", "start_time": "2024-03-12T15:55:57.847274Z" }, "collapsed": false }, "outputs": [], "source": [ "topic_info_path_out = os.path.join(output_subdir, 'topic_info_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "topic_info.to_csv(topic_info_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 19, "id": "cf9abfd89e3a73ea", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:56:02.095511Z", "start_time": "2024-03-12T15:56:02.091154Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('hotel', 0.016977601770727945),\n", " ('hcmc', 0.013088182131303217),\n", " ('la', 0.011691598611551321),\n", " ('siesta', 0.009376909680884539),\n", " ('nha', 0.008988156958020873),\n", " ('phu', 0.00868016573525121),\n", " ('quoc', 0.008069180456770169),\n", " ('loved', 0.0074425178865122395),\n", " ('trang', 0.007441576254724641),\n", " ('time', 0.007351373896307506)]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_model_tripadvisor.get_topic(0)" ] }, { "cell_type": "code", "execution_count": 20, "id": "c1f1d649e99944b", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:56:12.698465Z", "start_time": "2024-03-12T15:56:12.694106Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Main': {-1: [('hotel', 0.008358491315165379),\n", " ('staff', 0.0078031071088884),\n", " ('breakfast', 0.007050404746067847),\n", " ('clean', 0.006513424933049755),\n", " ('friendly', 0.006203175636383291),\n", " ('stayed', 0.00593851061126205),\n", " ('helpful', 0.005916034105684312),\n", " ('really', 0.005849939720737732),\n", " ('hanoi', 0.005399506006071156),\n", " ('time', 0.005288064116347683)],\n", " 0: [('hotel', 0.016977601770727945),\n", " ('hcmc', 0.013088182131303217),\n", " ('la', 0.011691598611551321),\n", " ('siesta', 0.009376909680884539),\n", " ('nha', 0.008988156958020873),\n", " ('phu', 0.00868016573525121),\n", " ('quoc', 0.008069180456770169),\n", " ('loved', 0.0074425178865122395),\n", " ('trang', 0.007441576254724641),\n", " ('time', 0.007351373896307506)],\n", " 1: [('friendly', 0.11666815200412975),\n", " ('staff', 0.08842720788238201),\n", " ('desk', 0.02316140866914839),\n", " ('owners', 0.02038254177638414),\n", " ('help', 0.01524847335272206),\n", " ('receptionists', 0.013032909591408383),\n", " ('concierge', 0.011832683692321454),\n", " ('amazing', 0.011387219669047167),\n", " ('speaks', 0.011247346927856347),\n", " ('accommodating', 0.010047138692387882)],\n", " 2: [('clean', 0.0710368303589109),\n", " ('rooms', 0.0654774781281693),\n", " ('spacious', 0.05474548202326031),\n", " ('executive', 0.017440007826933942),\n", " ('club', 0.015638445822832373),\n", " ('upgraded', 0.014282151711787839),\n", " ('small', 0.01023434471718046),\n", " ('aircon', 0.008787368847086639),\n", " ('service', 0.00797286171698336),\n", " ('lovely', 0.007831491784651838)],\n", " 3: [('bay', 0.06734208414886458),\n", " ('halong', 0.06082238910959701),\n", " ('sapa', 0.03247607443752786),\n", " ('booked', 0.022879763341944675),\n", " ('metropole', 0.016668949987305993),\n", " ('tours', 0.01464449060941835),\n", " ('puppet', 0.014157149045991748),\n", " ('flight', 0.012585554961013037),\n", " ('helped', 0.012206289823542145),\n", " ('tunnels', 0.010703021029943463)],\n", " 4: [('buffet', 0.05383265877379723),\n", " ('choices', 0.018735139381213895),\n", " ('rooftop', 0.016897903543202763),\n", " ('coffee', 0.016477398071701566),\n", " ('amazing', 0.015387325081882779),\n", " ('breakfasts', 0.013706774822050091),\n", " ('tasty', 0.012709691958578684),\n", " ('decent', 0.011260311635172349),\n", " ('wide', 0.010077420865203775),\n", " ('served', 0.009544822801371063)],\n", " 5: [('qualify', 1.5770983582870752),\n", " ('suschuc', 0.818637957298339),\n", " ('waow', 0.818637957298339),\n", " ('munz', 0.818637957298339),\n", " ('arggg', 0.818637957298339),\n", " ('salut', 0.7262192623564039),\n", " ('jesus', 0.5919004462614773),\n", " ('nb', 0.5256994527623584),\n", " ('boom', 0.4989557428041019),\n", " ('public', 0.30440092257195883)],\n", " 6: [('thank', 0.4149661547468614),\n", " ('ms', 0.03902896696281502),\n", " ('team', 0.035288803399738604),\n", " ('nguyen', 0.02086632628572446),\n", " ('thao', 0.019399219273560954),\n", " ('ngoc', 0.013788983463784033),\n", " ('ambassador', 0.013735654756288405),\n", " ('anna', 0.013302083119227861),\n", " ('lot', 0.012608248791153362),\n", " ('huong', 0.011303610619213722)],\n", " 7: [('christina', 0.04764738640358733),\n", " ('honeymoon', 0.03978573850579136),\n", " ('birthday', 0.03852351552255004),\n", " ('cake', 0.03021905359523636),\n", " ('moon', 0.025762083734874264),\n", " ('cinnamon', 0.021824275701542755),\n", " ('feel', 0.01596679760423027),\n", " ('staf', 0.015660971443791662),\n", " ('jolie', 0.01384416036719632),\n", " ('cherish', 0.012612098749522968)],\n", " 8: [('kiem', 0.10583768697639026),\n", " ('hoan', 0.10037699429273883),\n", " ('lake', 0.08394657422199203),\n", " ('quarter', 0.046172338644901764),\n", " ('ben', 0.036113094168895285),\n", " ('walking', 0.03444991622569683),\n", " ('town', 0.03365635773305934),\n", " ('dong', 0.025459075588114083),\n", " ('close', 0.024979041950579678),\n", " ('lao', 0.014075242342146143)],\n", " 9: [('red', 0.042392546702475296),\n", " ('halal', 0.04055332974755633),\n", " ('pho', 0.031519584041585155),\n", " ('restaurants', 0.031237545110338362),\n", " ('excellent', 0.01951321145429618),\n", " ('resort', 0.015739954252098527),\n", " ('chef', 0.012970320162954217),\n", " ('staff', 0.01254734260224399),\n", " ('vegetarian', 0.011874900679470543),\n", " ('service', 0.011063188267959628)],\n", " 10: [('convenient', 0.033503376050162924),\n", " ('fault', 0.022281290402436812),\n", " ('amazing', 0.015471023587682316),\n", " ('city', 0.014384156575720336),\n", " ('gem', 0.009684712904944645),\n", " ('nice', 0.008372248407477642),\n", " ('close', 0.008011975333612897),\n", " ('price', 0.006274823595969506),\n", " ('centre', 0.005813492180248835),\n", " ('friends', 0.0055481448725038796)],\n", " 11: [('windows', 0.0421405819850921),\n", " ('spotless', 0.03726437108283051),\n", " ('safe', 0.01602096633793928),\n", " ('worst', 0.014402782090751829),\n", " ('rooms', 0.013929625719214755),\n", " ('light', 0.013543685982854771),\n", " ('sheets', 0.013498930322138851),\n", " ('don', 0.012577644765984533),\n", " ('stains', 0.01047955847601273),\n", " ('wall', 0.010244660651155575)],\n", " 12: [('rooftop', 0.132311812985231),\n", " ('pool', 0.038754710731809826),\n", " ('views', 0.03216875981026076),\n", " ('drinks', 0.0213530334600516),\n", " ('minibar', 0.018982195128169007),\n", " ('city', 0.016863735711996408),\n", " ('hour', 0.016368589808305453),\n", " ('welcome', 0.014990776273000767),\n", " ('crawl', 0.013875245379810336),\n", " ('sunset', 0.012714492860651561)],\n", " 13: [('fowers', 0.33104076641715263),\n", " ('securities', 0.2588801435741562),\n", " ('replies', 0.20886914782439606),\n", " ('coupled', 0.19963382742162433),\n", " ('alway', 0.18716842254496738),\n", " ('entering', 0.1525521232874718),\n", " ('im', 0.14917362577965537),\n", " ('costs', 0.13837796721383025),\n", " ('write', 0.1253326726131298),\n", " ('la', 0.08328395695973367)],\n", " 14: [('bikes', 0.13325248184440827),\n", " ('free', 0.08384978066394626),\n", " ('scooter', 0.04227457998622575),\n", " ('town', 0.03680269417758244),\n", " ('beach', 0.026253395455253167),\n", " ('explore', 0.017399082385793095),\n", " ('old', 0.013310005846766506),\n", " ('easy', 0.012414397189276762),\n", " ('renting', 0.012383050021050284),\n", " ('minutes', 0.011939623423479193)],\n", " 15: [('service', 0.22843694903571005),\n", " ('impeccable', 0.04693177865735649),\n", " ('exceptional', 0.031189760081347686),\n", " ('notch', 0.026331108901022242),\n", " ('friendly', 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0.01460915214902486),\n", " ('provided', 0.012515344589135571),\n", " ('experience', 0.009362406592151177)],\n", " 93: [('cam', 2.6682260901714794),\n", " ('ơn', 1.3832169562540577),\n", " ('caaaamm', 0.14975084584725715),\n", " ('colmar', 0.13284498701641534),\n", " ('tuyet', 0.08539463659602582),\n", " ('ngan', 0.07272160554848452),\n", " ('phuc', 0.06371324408651993),\n", " ('nhi', 0.059386459371480654),\n", " ('little', 0.02037227216053807),\n", " ('', 1e-05)],\n", " 94: [('say', 2.142577093256741),\n", " ('talk', 0.06950161641505312),\n", " ('tell', 0.06501517328118395),\n", " ('said', 0.04716697445173475),\n", " ('thing', 0.041183093747759296),\n", " ('short', 0.03940563924304333),\n", " ('nice', 0.013038797684306373),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05)],\n", " 95: [('recommend', 1.130167471723465),\n", " ('return', 0.04485594751404198),\n", " ('recommended', 0.04005562064676764),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05)],\n", " 96: [('hesitate', 1.0060798037915601),\n", " ('don', 0.36837533784867127),\n", " ('fence', 0.044205528810423105),\n", " ('region', 0.03146078577435833),\n", " ('delighted', 0.029268635332236743),\n", " ('wrong', 0.022220940217552384),\n", " ('twice', 0.020684886650032427),\n", " ('miss', 0.019421672411056842),\n", " ('think', 0.015263046871753814),\n", " ('booking', 0.01305237175778658)],\n", " 97: [('soon', 1.5392031236561303),\n", " ('romana', 0.10089212152184274),\n", " ('month', 0.08661107332281714),\n", " ('chicland', 0.08602897788855215),\n", " ('chris', 0.07089642973872037),\n", " ('mary', 0.06282884905464897),\n", " ('beer', 0.036675690464223525),\n", " ('friends', 0.028579116660709666),\n", " ('visit', 0.022018742100315954),\n", " ('close', 0.019793035074515627)],\n", " 98: [('start', 3.0408663202582566),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05),\n", " ('', 1e-05)]}}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topic_keyword_weights = topic_model_tripadvisor.get_topics(full=True)\n", "topic_keyword_weights" ] }, { "cell_type": "code", "execution_count": null, "id": "e6a74ec619335680", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:57:11.737733Z", "start_time": "2024-03-12T15:57:11.727587Z" }, "collapsed": false }, "outputs": [], "source": [ "topic_keyword_weights_path_out = os.path.join(output_subdir, 'topic_keyword_weights_'+doc_type+'_'+doc_level+'_'+doc_time+'.json')\n", "with open(topic_keyword_weights_path_out, 'w', encoding=\"utf-8\") as f:\n", " f.write(json.dumps(str(topic_keyword_weights),indent=4, ensure_ascii=False))" ] }, { "cell_type": "code", "execution_count": 4, "id": "e395e7bf-3a20-4e10-baec-a287389e8169", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'topic_model_tripadvisor' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgensim\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcorpora\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mcorpora\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgensim\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcoherencemodel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CoherenceModel\n\u001b[0;32m----> 6\u001b[0m cleaned_docs \u001b[38;5;241m=\u001b[39m \u001b[43mtopic_model_tripadvisor\u001b[49m\u001b[38;5;241m.\u001b[39m_preprocess_text(df_documents_tripadvisor)\n\u001b[1;32m 7\u001b[0m vectorizer \u001b[38;5;241m=\u001b[39m topic_model_tripadvisor\u001b[38;5;241m.\u001b[39mvectorizer_model\n\u001b[1;32m 8\u001b[0m analyzer \u001b[38;5;241m=\u001b[39m vectorizer\u001b[38;5;241m.\u001b[39mbuild_analyzer()\n", "\u001b[0;31mNameError\u001b[0m: name 'topic_model_tripadvisor' is not defined" ] } ], "source": [ "# Coherence score\n", "import gensim.corpora as corpora\n", "from gensim.models.coherencemodel import CoherenceModel\n", "\n", "def check_valid(list_topics):\n", " count = 0\n", " for topic in list_topics:\n", " if topic[0] != '':\n", " count += 1\n", " \n", " return True if count > 2 else False\n", "\n", "cleaned_docs = topic_model_tripadvisor._preprocess_text(df_documents_tripadvisor)\n", "vectorizer = topic_model_tripadvisor.vectorizer_model\n", "analyzer = vectorizer.build_analyzer()\n", "tokens = [analyzer(doc) for doc in cleaned_docs]\n", "dictionary = corpora.Dictionary(tokens)\n", "corpus = [dictionary.doc2bow(token) for token in tokens]\n", "topics = topic_model_tripadvisor.get_topics()\n", "# topics.pop(-1, None)\n", "\n", "# topic_words = [\n", "# [word for word, _ in topic_model_tripadvisor.get_topic(topic) if word != \"\"] for topic in topics if len(topic) > 1\n", "# ]\n", "# topic_words = [[words for words, _ in topic_model_booking.get_topic(topic)] \n", "# for topic in range(len(set(topics))-1)]\n", "topic_words = [\n", " [word for word, _ in topic_model_tripadvisor.get_topic(topic) if word != \"\"] for topic in topics if check_valid(topic_model_tripadvisor.get_topic(topic))\n", " ]\n", "\n", " # Evaluate\n", "coherence_model = CoherenceModel(topics=topic_words, \n", " texts=tokens, \n", " corpus=corpus,\n", " dictionary=dictionary, \n", " coherence='c_npmi')\n", "coherence = coherence_model.get_coherence()\n", "coherence_path = os.path.join(output_subdir, 'coherence_score_tripadvisor'+'.txt')\n", "with open(coherence_path, 'w', encoding=\"utf-8\") as f:\n", " f.write(f'Coherence score: {coherence}')" ] }, { "cell_type": "code", "execution_count": null, "id": "828c14fac64e093d", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:57:16.305002Z", "start_time": "2024-03-12T15:57:15.047062Z" }, "collapsed": false }, "outputs": [], "source": [ "df_topics_tripadvisor = topic_model_tripadvisor.get_document_info(df_documents_tripadvisor)" ] }, { "cell_type": "code", "execution_count": null, "id": "cecf9878b432857b", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:57:22.247020Z", "start_time": "2024-03-12T15:57:21.661893Z" }, "collapsed": false }, "outputs": [], "source": [ "df_doc_out = pd.concat([df_topics_tripadvisor,df_doc_out_tripadvisor.loc[:,\"review_id\":]],axis=1)" ] }, { "cell_type": "code", "execution_count": null, "id": "12266c1c3fccea2c", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:57:24.219627Z", "start_time": "2024-03-12T15:57:24.205763Z" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DocumentTopicNameRepresentationRepresentative_DocsTop_n_wordsProbabilityRepresentative_documentreview_iddatemonthlyquarterlyyearly
0Good hotel i have ever stayed in Vietnam, good...-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False02023-07-312023-072023Q32023
1i will be back soon .-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False02023-07-312023-072023Q32023
2This place was very nice.-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False12023-05-312023-052023Q22023
3Our bedroom were clean and comfortable.22_spacious_comfortable_bathroom_dorms[spacious, comfortable, bathroom, dorms, spotl...[Rooms are clean and spacious., The rooms are ...spacious - comfortable - bathroom - dorms - sp...1.0False12023-05-312023-052023Q22023
4The bathtub was great and something you won’t ...-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False12023-05-312023-052023Q22023
..........................................
3316469The staff was very nice, spoke good English an...-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False5200062019-02-282019-022019Q12019
3316470My friend and I received excellent and profess...-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False5200072018-02-282018-022018Q12018
3316471Always with a smile for the customers.-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False5200072018-02-282018-022018Q12018
3316472Good place to sleep in Da Nang, they have also...-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False5200072018-02-282018-022018Q12018
3316473Cheap and satisfied hostel!-1-1_hotel_staff_breakfast_clean[hotel, staff, breakfast, clean, friendly, sta...[The service was friendly and room was clean.,...hotel - staff - breakfast - clean - friendly -...0.0False5200072018-02-282018-022018Q12018
\n", "

3316474 rows × 13 columns

\n", "
" ], "text/plain": [ " Document Topic \\\n", "0 Good hotel i have ever stayed in Vietnam, good... -1 \n", "1 i will be back soon . -1 \n", "2 This place was very nice. -1 \n", "3 Our bedroom were clean and comfortable. 2 \n", "4 The bathtub was great and something you won’t ... -1 \n", "... ... ... \n", "3316469 The staff was very nice, spoke good English an... -1 \n", "3316470 My friend and I received excellent and profess... -1 \n", "3316471 Always with a smile for the customers. -1 \n", "3316472 Good place to sleep in Da Nang, they have also... -1 \n", "3316473 Cheap and satisfied hostel! -1 \n", "\n", " Name \\\n", "0 -1_hotel_staff_breakfast_clean \n", "1 -1_hotel_staff_breakfast_clean \n", "2 -1_hotel_staff_breakfast_clean \n", "3 2_spacious_comfortable_bathroom_dorms \n", "4 -1_hotel_staff_breakfast_clean \n", "... ... \n", "3316469 -1_hotel_staff_breakfast_clean \n", "3316470 -1_hotel_staff_breakfast_clean \n", "3316471 -1_hotel_staff_breakfast_clean \n", "3316472 -1_hotel_staff_breakfast_clean \n", "3316473 -1_hotel_staff_breakfast_clean \n", "\n", " Representation \\\n", "0 [hotel, staff, breakfast, clean, friendly, sta... \n", "1 [hotel, staff, breakfast, clean, friendly, sta... \n", "2 [hotel, staff, breakfast, clean, friendly, sta... \n", "3 [spacious, comfortable, bathroom, dorms, spotl... \n", "4 [hotel, staff, breakfast, clean, friendly, sta... \n", "... ... \n", "3316469 [hotel, staff, breakfast, clean, friendly, sta... \n", "3316470 [hotel, staff, breakfast, clean, friendly, sta... \n", "3316471 [hotel, staff, breakfast, clean, friendly, sta... \n", "3316472 [hotel, staff, breakfast, clean, friendly, sta... \n", "3316473 [hotel, staff, breakfast, clean, friendly, sta... \n", "\n", " Representative_Docs \\\n", "0 [The service was friendly and room was clean.,... \n", "1 [The service was friendly and room was clean.,... \n", "2 [The service was friendly and room was clean.,... \n", "3 [Rooms are clean and spacious., The rooms are ... \n", "4 [The service was friendly and room was clean.,... \n", "... ... \n", "3316469 [The service was friendly and room was clean.,... \n", "3316470 [The service was friendly and room was clean.,... \n", "3316471 [The service was friendly and room was clean.,... \n", "3316472 [The service was friendly and room was clean.,... \n", "3316473 [The service was friendly and room was clean.,... \n", "\n", " Top_n_words Probability \\\n", "0 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "1 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "2 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "3 spacious - comfortable - bathroom - dorms - sp... 1.0 \n", "4 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "... ... ... \n", "3316469 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "3316470 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "3316471 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "3316472 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "3316473 hotel - staff - breakfast - clean - friendly -... 0.0 \n", "\n", " Representative_document review_id date monthly quarterly \\\n", "0 False 0 2023-07-31 2023-07 2023Q3 \n", "1 False 0 2023-07-31 2023-07 2023Q3 \n", "2 False 1 2023-05-31 2023-05 2023Q2 \n", "3 False 1 2023-05-31 2023-05 2023Q2 \n", "4 False 1 2023-05-31 2023-05 2023Q2 \n", "... ... ... ... ... ... \n", "3316469 False 520006 2019-02-28 2019-02 2019Q1 \n", "3316470 False 520007 2018-02-28 2018-02 2018Q1 \n", "3316471 False 520007 2018-02-28 2018-02 2018Q1 \n", "3316472 False 520007 2018-02-28 2018-02 2018Q1 \n", "3316473 False 520007 2018-02-28 2018-02 2018Q1 \n", "\n", " yearly \n", "0 2023 \n", "1 2023 \n", "2 2023 \n", "3 2023 \n", "4 2023 \n", "... ... \n", "3316469 2019 \n", "3316470 2018 \n", "3316471 2018 \n", "3316472 2018 \n", "3316473 2018 \n", "\n", "[3316474 rows x 13 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_doc_out" ] }, { "cell_type": "code", "execution_count": null, "id": "6baa3a2d98eb72f9", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:58:17.807143Z", "start_time": "2024-03-12T15:57:50.171718Z" }, "collapsed": false }, "outputs": [], "source": [ "df_doc_out_path = os.path.join(output_subdir, 'df_documents_tripadvisor'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "df_doc_out.to_csv(df_doc_out_path, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "463b2c77-92d7-45f5-923c-d02867403104", "metadata": {}, "outputs": [], "source": [ "# df_topic_info_metric_tripadvisor = topic_model_tripadvisor.get_topic_info_metric(docs = df_documents_tripadvisor ,df_doc_out_booking = df_doc_out_tripadvisor)\n", "# df_topic_info_metric_tripadvisor" ] }, { "cell_type": "code", "execution_count": null, "id": "963ba73d-39e1-4c79-8f3c-87dc76349b3e", "metadata": {}, "outputs": [], "source": [ "# df_topic_info_metric_path = os.path.join(output_subdir, 'df_topic_info_metric_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "# df_topic_info_metric_tripadvisor.to_csv(df_topic_info_metric_path, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "ee155f39089e7321", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:58:40.259345Z", "start_time": "2024-03-12T15:58:32.425109Z" }, "collapsed": false }, "outputs": [], "source": [ "df_doc_path = os.path.join(output_subdir, 'df_docs_tripadvisor'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "df_doc_tripadvisor.to_csv(df_doc_path, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "3750d38428c9f5e9", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:58:43.310784Z", "start_time": "2024-03-12T15:58:43.305460Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'calculate_probabilities': False,\n", " 'ctfidf_model': ClassTfidfTransformer(),\n", " 'embedding_model': ,\n", " 'hdbscan_model': HDBSCAN(),\n", " 'language': None,\n", " 'low_memory': True,\n", " 'min_topic_size': 10,\n", " 'n_gram_range': (1, 1),\n", " 'nr_topics': 100,\n", " 'representation_model': MaximalMarginalRelevance(diversity=0.7),\n", " 'seed_topic_list': None,\n", " 'top_n_words': 30,\n", " 'umap_model': UMAP(),\n", " 'vectorizer_model': CountVectorizer(stop_words='english'),\n", " 'verbose': True,\n", " 'zeroshot_min_similarity': 0.7,\n", " 'zeroshot_topic_list': None}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_params = topic_model_tripadvisor.get_params()\n", "model_params" ] }, { "cell_type": "code", "execution_count": null, "id": "9d737af06de5dccb", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:58:47.730514Z", "start_time": "2024-03-12T15:58:47.726860Z" }, "collapsed": false }, "outputs": [], "source": [ "model_params_path_txt_out = os.path.join(output_subdir, 'model_params_tripadvisor'+doc_type+'_'+doc_level+'_'+doc_time+'.txt')\n", "with open(model_params_path_txt_out, 'w', encoding=\"utf-8\") as f:\n", " f.write(json.dumps(str(model_params),indent=4, ensure_ascii=False))" ] }, { "cell_type": "code", "execution_count": null, "id": "5faa03f0a35be440", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:58:55.894785Z", "start_time": "2024-03-12T15:58:50.404280Z" }, "collapsed": false }, "outputs": [], "source": [ "fig = topic_model_tripadvisor.visualize_topics()\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "a21cddc64b8fc157", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T15:59:15.491386Z", "start_time": "2024-03-12T15:59:15.475127Z" }, "collapsed": false }, "outputs": [], "source": [ "vis_save_dir = os.path.join(output_subdir, 'bertopic_vis_tripadvisor'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", "fig.write_html(vis_save_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "7855901b3c286f9d", "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b067cc6a35dd8618", "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6641dd4e19396bcf", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:02:05.925346Z", "start_time": "2024-03-12T16:01:41.035173Z" }, "collapsed": false }, "outputs": [], "source": [ "# Hierarchical topics\n", "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html\n", "linkage_function = lambda x: sch.linkage(x, 'average', optimal_ordering=True)\n", "hierarchical_topics = topic_model_tripadvisor.hierarchical_topics(df_documents_tripadvisor, linkage_function=linkage_function)" ] }, { "cell_type": "code", "execution_count": null, "id": "8e9ad6a4968755ff", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:02:09.884542Z", "start_time": "2024-03-12T16:02:09.875886Z" }, "collapsed": false }, "outputs": [], "source": [ "hierarchical_topics" ] }, { "cell_type": "code", "execution_count": null, "id": "9e9ca1b42df77c7b", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:02:21.164501Z", "start_time": "2024-03-12T16:02:21.158962Z" }, "collapsed": false }, "outputs": [], "source": [ "hierarchical_topics['Topics'].apply(len)>1" ] }, { "cell_type": "code", "execution_count": null, "id": "65b7021a10ad4346", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:03:11.848585Z", "start_time": "2024-03-12T16:03:11.844052Z" }, "collapsed": false }, "outputs": [], "source": [ "hierarchical_topics.loc[hierarchical_topics['Topics'].apply(len)>1].Parent_Name" ] }, { "cell_type": "code", "execution_count": null, "id": "f72c6e69293bfd11", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:04:46.290254Z", "start_time": "2024-03-12T16:04:46.274223Z" }, "collapsed": false }, "outputs": [], "source": [ "hierarchical_topics_path_out = os.path.join(output_subdir, 'hierarchical_topics_path_out_tripadvisor'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')\n", "hierarchical_topics.to_csv(hierarchical_topics_path_out, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "id": "b14184911a51fd00", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:04:50.364140Z", "start_time": "2024-03-12T16:04:50.299525Z" }, "collapsed": false }, "outputs": [], "source": [ "fig = topic_model_tripadvisor.visualize_hierarchy(hierarchical_topics=hierarchical_topics)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "cc3eb65006de5de3", "metadata": { "ExecuteTime": { "end_time": "2024-03-12T16:05:01.114594Z", "start_time": "2024-03-12T16:05:01.091632Z" }, "collapsed": false }, "outputs": [], "source": [ "vis_save_dir = os.path.join(output_subdir, 'bertopic_hierarchy_vis_tripadvisor'+doc_type+'_'+doc_level+'_'+doc_time+'.html')\n", "fig.write_html(vis_save_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": 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Words: clean, spacious, comfortable, bathroom, dorms", "Topic 2
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Words: especailly, min, kasamnida, mabuhay, buhuuu", "Topic 4
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Words: kasamnida, mabuhay, buhuuu, שלום, crazzy", "Topic 4
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Words: hoan, perfume, river, kiem, 70km", "Topic 5
Words: boundaries, headquarters, kiem, hoan, lakes", "Topic 5
Words: buoi, lake, kiem, xuan, huong", "Topic 5
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Words: kiem, hoan, mtr, tropicana, canoeing", "Topic 5
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Words: kiem, hoan, metropole, river, heaters", "Topic 5
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Words: kiếm, hoàn, step, lake, hotel", "Topic 5
Words: lake, perfume, dislike, cockroach, overlooks", "Topic 5
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Words: westlake, picturesque, intercontinental, opportunity, long", "Topic 5
Words: quarantine, tuyen, lam, resort, hoan", "Topic 5
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Words: forest, beachfront, thuy, van, minutes", "Topic 5
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Words: terraco, kiem, hoan, breathtaking, rooftop", "Topic 5
Words: imperial, kiem, hoan, beauitul, river", "Topic 5
Words: imperial, kiem, hoan, taco, riverbank", "Topic 5
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Words: kiem, lake, hoan, walk, close", "Topic 5
Words: lake, kiem, hoan, walking, metropole", "Topic 5
Words: hoan, kiem, river, ben, metropole", "Topic 5
Words: kiem, hoan, river, perfume, beach", "Topic 5
Words: lake, hoan, kiem, perfume, walking", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: hoan, kiem, lake, walking, perfume", "Topic 5
Words: lake, kiem, hoan, walk, coc", "Topic 5
Words: kiem, hoan, river, walk, ben", "Topic 5
Words: lake, kiem, hoan, perfume, walking", "Topic 5
Words: lake, kiem, hoan, close, walking", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
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Words: airport, shuttle, noi, puppet, booked", "Topic 6
Words: shuttle, noi, tunnels, bomb, tours", "Topic 6
Words: puppet, shuttle, noi, cu, tunnels", "Topic 6
Words: noi, puppet, airport, tunnels, agoda", "Topic 6
Words: shuttle, noi, tunnels, tours, chi", "Topic 6
Words: cu, noi, bus, tours, puppet", "Topic 6
Words: noi, cu, tunnels, puppet, booking", "Topic 6
Words: noi, bomb, puppet, airport, cu", "Topic 6
Words: noi, shuttle, airport, chi, bunker", "Topic 6
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Words: puppet, shuttle, noi, airport, tours", "Topic 6
Words: noi, free, bomb, tunnels, travel", "Topic 6
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Words: noi, airport, puppet, tours, agoda", "Topic 6
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Words: noi, puppet, tours, luggage, passports", "Topic 6
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Words: shuttle, noi, tours, tunnels, booked", "Topic 6
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Words: noi, puppet, cu, tunnels, booking", "Topic 6
Words: airport, shuttle, noi, tours, booked", "Topic 6
Words: noi, airport, puppet, tours, cu", "Topic 6
Words: noi, puppet, airport, tram, tunnels", "Topic 6
Words: noi, passport, tours, taxi, cu", "Topic 6
Words: noi, puppet, tours, shelter, chi", "Topic 6
Words: noi, puppet, airport, tours, cu", "Topic 6
Words: noi, tours, passport, flight, chi", "Topic 6
Words: noi, airport, puppet, cu, tunnels", "Topic 6
Words: shuttle, puppet, booking, free, cu", "Topic 6
Words: noi, shuttle, puppet, booking, cu", "Topic 6
Words: noi, luggage, puppet, tours, bus", "Topic 6
Words: noi, passports, tours, booked, town", "Topic 6
Words: noi, booked, tours, bomb, town", "Topic 6
Words: noi, puppet, airport, tunnels, tours", "Topic 6
Words: shuttle, airport, noi, puppet, castaways", "Topic 6
Words: tour, noi, airport, puppet, chi", "Topic 6
Words: shuttle, noi, puppet, airport, free", "Topic 6
Words: puppet, noi, phuc, booked, guide", "Topic 6
Words: noi, booking, phuc, passport, theatre", "Topic 6
Words: noi, shuttle, bomb, phuc, tours", "Topic 6
Words: noi, puppet, tours, phuc, booked", "Topic 6
Words: noi, phuc, booking, ha, guide", "Topic 6
Words: noi, shuttle, booking, cu, tunnels", "Topic 6
Words: noi, airport, phuc, puppet, tours", "Topic 6
Words: noi, cu, puppet, luggage, bus", "Topic 6
Words: noi, puppet, phuc, booked, guide", "Topic 6
Words: phuc, noi, bomb, guide, tunnels", "Topic 6
Words: noi, tours, agoda, phuc, refund", "Topic 6
Words: qantas, minibus, confirmation, passports, grand", "Topic 6
Words: erik, supported, tours, noi, airport", "Topic 6
Words: exited, melbourne, swiming, 15th, luggages", "Topic 6
Words: dislocation, graciously, tram, crowded, airport", "Topic 6
Words: noi, travel, shuttle, puppet, tunnels", "Topic 6
Words: agoda, noi, shuttle, nearby, spacious", "Topic 6
Words: zao, raincoats, artifacts, bunker, shuttles", "Topic 6
Words: tram, anw, noi, partners, preference", "Topic 6
Words: tram, noi, puppet, tours, tunnels", "Topic 6
Words: illustrious, shelter, noi, airport, drop", "Topic 6
Words: hiding, costly, 1night, passports, refused", "Topic 6
Words: noi, step, booking, tunnels, taxi", "Topic 6
Words: grand, tram, agoda, noi, puppet", "Topic 6
Words: trapped, bird, luan, calling, taxi", "Topic 6
Words: sim, suggested, bought, cost, noi", "Topic 6
Words: copy, passports, noi, booking, tunnels", "Topic 6
Words: introduction, locality, eateries, noi, puppet", "Topic 6
Words: noi, tu, yen, mountains, step", "Topic 6
Words: forgotten, passport, shout, jenny, team", "Topic 6
Words: offended, attitude, decided, noi, airport", "Topic 6
Words: signature, luggage, noi, puppet, booking", "Topic 6
Words: noi, luggages, unpacked, permission, leah", "Topic 6
Words: noi, climates, stranger, airport, insisting", "Topic 6
Words: intercity, tournament, prompting, puppet, tours", "Topic 6
Words: noi, tour, dough, citycenter, comback", "Topic 6
Words: noi, ha, tram, bunker, meagre", "Topic 6
Words: noi, passports, taxi, graceful, puppet", "Topic 6
Words: noi, ha, tours, agoda, booked", "Topic 6
Words: noi, puppet, airport, shuttle, theatre", "Topic 6
Words: noi, puppet, theater, tram, 2022", "Topic 6
Words: noi, shuttle, ha, airport, cancelled", "Topic 6
Words: airport, tours, theatre, tripadvisor, differnet", "Topic 6
Words: noi, shelter, agoda, buses, town", "Topic 6
Words: noi, airport, ha, puppet, tram", "Topic 6
Words: noi, tours, shower, allowed, famose", "Topic 6
Words: noi, ha, puppet, tram, flight", "Topic 6
Words: noi, puppet, tre, booking, taxi", "Topic 6
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Words: location, central, relaxing, fault, amazing", "Topic 7
Words: location, relaxing, central, say, amazing", "Topic 7
Words: recommend, central, relax, amazing, brekfeast", "Topic 7
Words: location, relaxing, central, fault, holuday", "Topic 7
Words: location, relax, fault, amazing, confidential", "Topic 7
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Words: location, nook, atmosphere, relaxing, amazing", "Topic 7
Words: location, relax, amazing, atmosphere, perfict", "Topic 7
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Words: location, central, atmosphere, fault, relaxing", "Topic 7
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Words: location, fault, relaxing, amazing, say", "Topic 7
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Words: location, central, relax, fault, atmosphere", "Topic 7
Words: location, amazing, fault, centrally, vibe", "Topic 7
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Words: location, fault, central, relax, atmosphere", "Topic 7
Words: location, central, fault, relaxing, oasis", "Topic 7
Words: location, recommend, relaxing, fault, atmosphere", "Topic 7
Words: location, relaxing, central, amazing, recharge", "Topic 7
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Words: location, convenient, central, relax, atmosphere", "Topic 7
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Words: location, fault, relaxing, gem, amazing", "Topic 7
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Words: location, fault, central, relaxing, nook", "Topic 7
Words: location, fault, central, relax, rily", "Topic 7
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Words: loved, relax, fault, locations, central", "Topic 7
Words: location, relaxing, central, fault, amazing", "Topic 7
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Words: location, fault, atmosphere, amazing, relaxing", "Topic 7
Words: location, great, relaxing, central, atmosphere", "Topic 7
Words: excellent, whatsmore, tranquility, retreat, centrally", "Topic 7
Words: location, relaxing, central, evetyone, laybacks", "Topic 7
Words: solemn, peacefulness, birds, atmosphere, vacation", "Topic 7
Words: shop, central, place, coffee, restaurant", "Topic 7
Words: location, buck, advantage, atmosphere, relaxing", "Topic 7
Words: keangnam, place, landmark, vincom, lotte", "Topic 7
Words: pride, amazing, run, city, recommend", "Topic 7
Words: chilled, location, ideal, central, relaxing", "Topic 7
Words: aiport, annual, workers, retreat, company", "Topic 7
Words: strikes, location, want, people, visit", "Topic 7
Words: location, refresh, complaint, traveller, atmosphere", "Topic 7
Words: location, book, time, nice, convenient", "Topic 7
Words: photogenic, location, tasteful, travelers, holiday", "Topic 7
Words: freshup, location, engaged, justice, zone", "Topic 7
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Words: location, convenient, peaceful, fault, amazing", "Topic 7
Words: convenient, location, peaceful, fault, amazing", "Topic 7
Words: wouldn, peaceful, know, location, convenient", "Topic 7
Words: elegance, professionalism, location, cozy, times", "Topic 7
Words: ambiance, nature, location, relax, convenient", "Topic 7
Words: location, ages, 100, needs, central", "Topic 7
Words: getaway, architecture, weekend, location, recommend", "Topic 7
Words: location, tastefully, decorated, love, modern", "Topic 7
Words: location, wildlife, scenery, neighborhood, highly", "Topic 7
Words: central, iconic, eden, relaxation, atmosphere", "Topic 7
Words: location, spiritual, evidence, 20min, practical", "Topic 7
Words: location, overprice, waterfall, bakery, relaxing", "Topic 7
Words: location, perfect, relaxing, central, touris", "Topic 7
Words: convenient, amazing, ver, fault, central", "Topic 7
Words: location, centrally, patern, chillie, hip", "Topic 7
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Words: location, excellent, atmosphere, vine, centric", "Topic 7
Words: location, central, amazing, chong, relax", "Topic 7
Words: location, bitcold, fault, release, relaxing", "Topic 7
Words: location, amazing, central, fault, relaxation", "Topic 7
Words: location, relaxing, recommend, central, accesibilty", "Topic 7
Words: location, retreat, favourit, oasis, chillout", "Topic 7
Words: location, relaxing, highlt, central, skydeck", "Topic 7
Words: 30ins, retreat, chensea, sightseeings, centrally", "Topic 7
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Words: kasamnida, mabuhay, שלום, buhuuu, crazzy", "Topic 4
Words: kasamnida, mabuhay, buhuuu, שלום, crazzy", "Topic 4
Words: kasamnida, mabuhay, buhuuu, שלום, crazzy", "Topic 4
Words: kasamnida, mabuhay, buhuuu, שלום, crazzy", "Topic 4
Words: especailly, min, kasamnida, mabuhay, buhuuu", "Topic 4
Words: kasamnida, mabuhay, buhuuu, שלום, crazzy", "Topic 4
Words: kasamnida, mabuhay, buhuuu, שלום, crazzy", "Topic 4
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Words: lake, kiem, hoan, perfume, ben", "Topic 5
Words: lake, kiem, hoan, perfume, walking", "Topic 5
Words: lake, perfume, kiem, hoan, ben", "Topic 5
Words: kiem, river, hoan, perfume, ben", "Topic 5
Words: kiem, hoan, river, ben, perfume", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: kiem, hoan, river, perfume, ben", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: kiem, hoan, walk, perfume, close", "Topic 5
Words: kiem, hoan, river, walk, ben", "Topic 5
Words: lake, kiem, hoan, walking, ben", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: lake, kiem, hoan, walking, perfume", "Topic 5
Words: river, kiem, hoan, perfume, walking", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: lake, kiem, hoan, walk, ben", "Topic 5
Words: kiem, hoan, river, walking, location", "Topic 5
Words: hoan, kiem, river, perfume, walking", "Topic 5
Words: lake, kiem, hoan, market, ben", "Topic 5
Words: lake, kiem, hoan, market, ben", "Topic 5
Words: kiem, hoan, walking, river, ben", "Topic 5
Words: kiem, hoan, river, ben, perfume", "Topic 5
Words: lake, kiem, hoan, walk, perfume", "Topic 5
Words: kiem, hoan, ben, river, walking", "Topic 5
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Words: kiem, hoan, river, walking, perfume", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: kiem, hoan, river, ben, walking", "Topic 5
Words: kiem, hoan, river, walking, ben", "Topic 5
Words: lake, kiem, perfume, distance, ben", "Topic 5
Words: kiem, hoan, river, ben, walking", "Topic 5
Words: lake, kiem, hoan, market, close", "Topic 5
Words: kiem, hoan, river, walking, ben", "Topic 5
Words: lake, kiem, hoan, walking, perfume", "Topic 5
Words: lake, kiem, hoan, walk, minutes", "Topic 5
Words: kiem, hoan, river, perfume, ben", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: lake, kiem, hoan, market, walking", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: kiem, hoan, river, walking, ben", "Topic 5
Words: lake, kiem, hoan, walking, perfume", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: lake, kiem, hoan, market, walking", "Topic 5
Words: kiem, hoan, river, perfume, ben", "Topic 5
Words: lake, kiem, hoan, walking, perfume", "Topic 5
Words: kiem, hoan, market, river, walking", "Topic 5
Words: lake, hoan, kiem, ben, walking", "Topic 5
Words: lake, kiem, hoan, perfume, walking", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: kiem, hoan, river, walking, ben", "Topic 5
Words: kiem, hoan, walk, river, perfume", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: kiem, hoan, walk, close, perfume", "Topic 5
Words: kiem, hoan, river, ben, walking", "Topic 5
Words: lake, kiem, market, walking, xuan", "Topic 5
Words: lake, kiem, hoan, walking, perfume", "Topic 5
Words: kiem, lake, hoan, close, perfume", "Topic 5
Words: kiem, hoan, river, perfume, beach", "Topic 5
Words: kiem, hoan, river, walking, ben", "Topic 5
Words: lake, kiem, hoan, perfume, walking", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
Words: perfume, river, kiem, hoan, waddle", "Topic 5
Words: hoan, perfume, river, kiem, 70km", "Topic 5
Words: boundaries, headquarters, kiem, hoan, lakes", "Topic 5
Words: buoi, lake, kiem, xuan, huong", "Topic 5
Words: nhon, quy, hills, occasions, river", "Topic 5
Words: kiem, hoan, mtr, tropicana, canoeing", "Topic 5
Words: kiem, hoan, shallow, crystal, minutes", "Topic 5
Words: metropole, kiem, hoan, beach, reporting", "Topic 5
Words: kiem, hoan, metropole, river, heaters", "Topic 5
Words: imperial, kiem, hoan, talking, times", "Topic 5
Words: windy, beach, kiem, hoan, river", "Topic 5
Words: kiếm, hoàn, step, lake, hotel", "Topic 5
Words: lake, perfume, dislike, cockroach, overlooks", "Topic 5
Words: kiếm, hoàn, distance, cosy, terrace", "Topic 5
Words: westlake, picturesque, intercontinental, opportunity, long", "Topic 5
Words: quarantine, tuyen, lam, resort, hoan", "Topic 5
Words: west, neat, beach, kiem, hoan", "Topic 5
Words: forest, beachfront, thuy, van, minutes", "Topic 5
Words: metropole, staffes, views, coconut, gorgeous", "Topic 5
Words: terraco, kiem, hoan, breathtaking, rooftop", "Topic 5
Words: imperial, kiem, hoan, beauitul, river", "Topic 5
Words: imperial, kiem, hoan, taco, riverbank", "Topic 5
Words: lake, kiem, hoan, walk, close", "Topic 5
Words: kiem, lake, hoan, walk, close", "Topic 5
Words: lake, kiem, hoan, walking, metropole", "Topic 5
Words: hoan, kiem, river, ben, metropole", "Topic 5
Words: kiem, hoan, river, perfume, beach", "Topic 5
Words: lake, hoan, kiem, perfume, walking", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: kiem, hoan, river, walk, perfume", "Topic 5
Words: hoan, kiem, lake, walking, perfume", "Topic 5
Words: lake, kiem, hoan, walk, coc", "Topic 5
Words: kiem, hoan, river, walk, ben", "Topic 5
Words: lake, kiem, hoan, perfume, walking", "Topic 5
Words: lake, kiem, hoan, close, walking", "Topic 5
Words: kiem, hoan, river, perfume, walking", "Topic 5
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Words: airport, shuttle, noi, puppet, booked", "Topic 6
Words: shuttle, noi, tunnels, bomb, tours", "Topic 6
Words: puppet, shuttle, noi, cu, tunnels", "Topic 6
Words: noi, puppet, airport, tunnels, agoda", "Topic 6
Words: shuttle, noi, tunnels, tours, chi", "Topic 6
Words: cu, noi, bus, tours, puppet", "Topic 6
Words: noi, cu, tunnels, puppet, booking", "Topic 6
Words: noi, bomb, puppet, airport, cu", "Topic 6
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Words: shuttle, noi, tours, tunnels, booked", "Topic 6
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Words: airport, shuttle, noi, tours, booked", "Topic 6
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Words: noi, puppet, airport, tunnels, tours", "Topic 6
Words: shuttle, airport, noi, puppet, castaways", "Topic 6
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Words: noi, tours, agoda, phuc, refund", "Topic 6
Words: qantas, minibus, confirmation, passports, grand", "Topic 6
Words: erik, supported, tours, noi, airport", "Topic 6
Words: exited, melbourne, swiming, 15th, luggages", "Topic 6
Words: dislocation, graciously, tram, crowded, airport", "Topic 6
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Words: agoda, noi, shuttle, nearby, spacious", "Topic 6
Words: zao, raincoats, artifacts, bunker, shuttles", "Topic 6
Words: tram, anw, noi, partners, preference", "Topic 6
Words: tram, noi, puppet, tours, tunnels", "Topic 6
Words: illustrious, shelter, noi, airport, drop", "Topic 6
Words: hiding, costly, 1night, passports, refused", "Topic 6
Words: noi, step, booking, tunnels, taxi", "Topic 6
Words: grand, tram, agoda, noi, puppet", "Topic 6
Words: trapped, bird, luan, calling, taxi", "Topic 6
Words: sim, suggested, bought, cost, noi", "Topic 6
Words: copy, passports, noi, booking, tunnels", "Topic 6
Words: introduction, locality, eateries, noi, puppet", "Topic 6
Words: noi, tu, yen, mountains, step", "Topic 6
Words: forgotten, passport, shout, jenny, team", "Topic 6
Words: offended, attitude, decided, noi, airport", "Topic 6
Words: signature, luggage, noi, puppet, booking", "Topic 6
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Words: noi, climates, stranger, airport, insisting", "Topic 6
Words: intercity, tournament, prompting, puppet, tours", "Topic 6
Words: noi, tour, dough, citycenter, comback", "Topic 6
Words: noi, ha, tram, bunker, meagre", "Topic 6
Words: noi, passports, taxi, graceful, puppet", "Topic 6
Words: noi, ha, tours, agoda, booked", "Topic 6
Words: noi, puppet, airport, shuttle, theatre", "Topic 6
Words: noi, puppet, theater, tram, 2022", "Topic 6
Words: noi, shuttle, ha, airport, cancelled", "Topic 6
Words: airport, tours, theatre, tripadvisor, differnet", "Topic 6
Words: noi, shelter, agoda, buses, town", "Topic 6
Words: noi, airport, ha, puppet, tram", "Topic 6
Words: noi, tours, shower, allowed, famose", "Topic 6
Words: noi, ha, puppet, tram, flight", "Topic 6
Words: noi, puppet, tre, booking, taxi", "Topic 6
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Words: location, fault, central, relax, atmosphere", "Topic 7
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Words: location, relaxing, central, amazing, nook", "Topic 7
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Words: location, fault, relaxing, gem, amazing", "Topic 7
Words: location, relaxing, central, amazing, fault", "Topic 7
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Words: shop, central, place, coffee, restaurant", "Topic 7
Words: location, buck, advantage, atmosphere, relaxing", "Topic 7
Words: keangnam, place, landmark, vincom, lotte", "Topic 7
Words: pride, amazing, run, city, recommend", "Topic 7
Words: chilled, location, ideal, central, relaxing", "Topic 7
Words: aiport, annual, workers, retreat, company", "Topic 7
Words: strikes, location, want, people, visit", "Topic 7
Words: location, refresh, complaint, traveller, atmosphere", "Topic 7
Words: location, book, time, nice, convenient", "Topic 7
Words: photogenic, location, tasteful, travelers, holiday", "Topic 7
Words: freshup, location, engaged, justice, zone", "Topic 7
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Words: convenient, location, peaceful, fault, amazing", "Topic 7
Words: wouldn, peaceful, know, location, convenient", "Topic 7
Words: elegance, professionalism, location, cozy, times", "Topic 7
Words: ambiance, nature, location, relax, convenient", "Topic 7
Words: location, ages, 100, needs, central", "Topic 7
Words: getaway, architecture, weekend, location, recommend", "Topic 7
Words: location, tastefully, decorated, love, modern", "Topic 7
Words: location, wildlife, scenery, neighborhood, highly", "Topic 7
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Words: location, spiritual, evidence, 20min, practical", "Topic 7
Words: location, overprice, waterfall, bakery, relaxing", "Topic 7
Words: location, perfect, relaxing, central, touris", "Topic 7
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Words: location, centrally, patern, chillie, hip", "Topic 7
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Words: location, excellent, atmosphere, vine, centric", "Topic 7
Words: location, central, amazing, chong, relax", "Topic 7
Words: location, bitcold, fault, release, relaxing", "Topic 7
Words: location, amazing, central, fault, relaxation", "Topic 7
Words: location, relaxing, recommend, central, accesibilty", "Topic 7
Words: location, retreat, favourit, oasis, chillout", "Topic 7
Words: location, relaxing, highlt, central, skydeck", "Topic 7
Words: 30ins, retreat, chensea, sightseeings, centrally", "Topic 7
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Words: ruiz, thanksaaaaaaaaaaaaaaaaaaaaa, stado, cairns, manager", "Topic 8
Words: philip, guys, reilly, thuyan, lot", "Topic 8
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Words: thank, buckinghamshire, anna, tony, huong", "Topic 8
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Words: thank, ms, manager, nguyen, marco", "Topic 8
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Words: thank, adam, instructions, trung, time", "Topic 8
Words: thank, 29th, 2020, anna, lot", "Topic 8
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Words: thank, restrictions, thao, despite, ms", "Topic 8
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Words: thank, hospitality, manager, nguyen, anna", "Topic 8
Words: thank, 539, michaela, uyen, warming", "Topic 8
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Words: mercure, thanks, anh, phong, ms", "Topic 8
Words: thank, daniel, event, hospitality, manager", "Topic 8
Words: thank, quyền, phương, tuan, hospitality", "Topic 8
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Words: thank, trung, huong, nguyen, shawn", "Topic 8
Words: thank, quan, hospitality, margherita, winnie", "Topic 8
Words: thank, zschiesche, ardent, sẻvice, hospitality", "Topic 8
Words: thank, mai, vinh, hospitality, khang", "Topic 8
Words: thank, molly, nguyen, tommy, ngoc", "Topic 8
Words: thank, molly, elsa, thao, nguyen", "Topic 8
Words: thank, ngo, nguyen, foraigner, waitress", "Topic 8
Words: thank, hospitality, molly, nguyen, trungh", "Topic 8
Words: thank, ms, trang, thao, uyen", "Topic 8
Words: thank, nguyen, molly, tony, shuong", "Topic 8
Words: thank, thao, hospitality, lucy, manager", "Topic 8
Words: thank, nguyen, thao, tuan, hospitality", "Topic 8
Words: thank, thao, hospitality, nhu, nguyen", "Topic 8
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Words: spa, swimming, treatments, nice, equipment", "Topic 9
Words: pool, massages, area, treatment, fitness", "Topic 9
Words: pool, massages, tai, day, small", "Topic 9
Words: spa, treatments, swimming, jacuzzi, small", "Topic 9
Words: pool, massages, nice, jacuzzi, tai", "Topic 9
Words: massages, treatment, nice, area, balcony", "Topic 9
Words: spa, swimming, treatments, kids, small", "Topic 9
Words: massages, swimming, jacuzzi, overlooking, balcony", "Topic 9
Words: pool, massages, yoga, nice, facial", "Topic 9
Words: massages, area, jacuzzi, classes, tai", "Topic 9
Words: spa, jacuzzi, yoga, pools, nice", "Topic 9
Words: spa, swimming, jacuzzi, equipment, treatments", "Topic 9
Words: spa, swimming, treatments, jacuzzi, sauna", "Topic 9
Words: massage, jacuzzi, nice, small, saunas", "Topic 9
Words: massage, jacuzzi, equipment, cardio, small", "Topic 9
Words: jacuzzi, massages, yoga, area, free", "Topic 9
Words: pool, yoga, massages, jacuzzi, day", "Topic 9
Words: massage, swimming, nice, sauna, facilities", "Topic 9
Words: jacuzzi, massages, pools, fitness, equipment", "Topic 9
Words: spa, jacuzzi, treatments, yoga, small", "Topic 9
Words: jacuzzi, massages, facial, treatment, area", "Topic 9
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Words: spa, pool, treatments, therapist, sauna", "Topic 9
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Words: pool, massages, fitness, equipment, treatment", "Topic 9
Words: pool, massages, sauna, nice, daily", "Topic 9
Words: spa, jacuzzi, swimming, nice, balcony", "Topic 9
Words: massages, jacuzzi, fitness, area, facial", "Topic 9
Words: massages, jacuzzi, nice, area, pools", "Topic 9
Words: spa, yoga, treatments, pools, kids", "Topic 9
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Words: pool, massages, nice, sauna, treatment", "Topic 9
Words: massages, fitness, jacuzzi, pools, equipment", "Topic 9
Words: massages, jacuzzi, pools, nice, area", "Topic 9
Words: spa, jacuzzi, nice, pools, fitness", "Topic 9
Words: massages, jacuzzi, lovely, treatment, area", "Topic 9
Words: pool, massages, amazing, nice, facial", "Topic 9
Words: pool, massages, fitness, relaxing, jacuzzi", "Topic 9
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Words: massages, swimming, jacuzzi, area, use", "Topic 9
Words: massages, area, balcony, pools, treatment", "Topic 9
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Words: pool, yoga, massages, treatment, area", "Topic 9
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Words: pool, massages, small, nice, fitness", "Topic 9
Words: spa, pool, yoga, equipment, area", "Topic 9
Words: massages, swimming, nice, area, treatment", "Topic 9
Words: massage, cazame, jacuzzi, area, tried", "Topic 9
Words: pool, massages, enjoyed, jacuzzi, facial", "Topic 9
Words: massage, jacuzzi, nice, pools, 7pm", "Topic 9
Words: pool, yoga, jacuzzi, massages, relaxing", "Topic 9
Words: spa, yoga, treatments, small, pools", "Topic 9
Words: spa, fraction, skip, overpriced, refreshing", "Topic 9
Words: spa, closure, corona, onsen, pools", "Topic 9
Words: jacuzzi, nhien, therapists, 7pm, phenomenal", "Topic 9
Words: poolarea, love, polluted, classes, chilling", "Topic 9
Words: spa, difficulties, myst, treatments, advance", "Topic 9
Words: tub, pools, overlooking, big, massages", "Topic 9
Words: ropes, bells, pool, jump, treatment", "Topic 9
Words: weights, intend, insects, chefs, jacuzzi", "Topic 9
Words: saunas, victoria, forgotten, waste, upstairs", "Topic 9
Words: spa, reduce, sunbathing, yoga, equipment", "Topic 9
Words: spa, shadow, treatments, warmer, assortment", "Topic 9
Words: spa, notices, skillful, 7pm, disappointment", "Topic 9
Words: spa, sanskrit, skillful, happiness, classes", "Topic 9
Words: spa, winny, onsen, pool, sauna", "Topic 9
Words: onsen, guard, spas, meters, spring", "Topic 9
Words: tried, spa, treatments, jacuzzi, relaxing", "Topic 9
Words: independent, jacuzzi, building, resort, nice", "Topic 9
Words: dirty, gym, lounge, staffs, small", "Topic 9
Words: buzzer, caps, massage, awful, thao", "Topic 9
Words: beginners, massage, teacher, sparkling, leading", "Topic 9
Words: spa, ambience, rates, incredible, area", "Topic 9
Words: massage, shaded, leads, palm, included", "Topic 9
Words: massages, allow, highlights, splendid, weather", "Topic 9
Words: massage, saltwater, gyms, attendants, huong", "Topic 9
Words: pool, massages, teacher, nice, mantained", "Topic 9
Words: massages, beachh, fushionista, curve, salt", "Topic 9
Words: pool, frangipane, ánh, screened, massages", "Topic 9
Words: jacuzzi, massages, l4, confortably, cúc", "Topic 9
Words: pool, yoga, daily, inviting, professional", "Topic 9
Words: pool, massages, mit, nice, rejuvenating", "Topic 9
Words: massage, sauna, yogini, aarti, metta", "Topic 9
Words: pool, massages, vietnamesisch, meggie, enjoyable", "Topic 9
Words: pool, massages, sauna, teacher, rejuvenating", "Topic 9
Words: pool, massages, merkado, advantage, giri", "Topic 9
Words: pool, yoga, massages, 1200, equipped", "Topic 9
Words: spa, yoga, sauna, beautiful, small", "Topic 9
Words: gym, massages, jacuzzi, area, outdoor", "Topic 9
Words: pool, massages, nice, jacuzzi, use", "Topic 9
Words: massages, pools, sauna, area, discount", "Topic 9
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