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2 _ Topic Modeling/LDA.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "305b0a04",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "305b0a04",
+        "outputId": "e7dc6e25-3630-4ae2-faaf-8c82f3352807"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package stopwords to\n",
+            "[nltk_data]     C:\\Users\\vldth\\AppData\\Roaming\\nltk_data...\n",
+            "[nltk_data]   Package stopwords is already up-to-date!\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "from pprint import pprint\n",
+        "import nltk\n",
+        "from nltk.corpus import stopwords\n",
+        "from gensim.utils import simple_preprocess\n",
+        "from gensim.models import Phrases, LdaModel\n",
+        "from gensim.models.phrases import Phraser\n",
+        "from gensim import corpora\n",
+        "from gensim.models.coherencemodel import CoherenceModel\n",
+        "import pyLDAvis\n",
+        "import pyLDAvis.gensim_models as gensimvis\n",
+        "\n",
+        "nltk.download('stopwords')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "20eb23e6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "20eb23e6",
+        "outputId": "92a2558c-ea3f-40a9-9830-ad1c577595e5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Original dataset:\n",
+            "                                              review  sentiment\n",
+            "0  at first gumagana cya..pero pagnalowbat cya nd...          1\n",
+            "1  grabi pangalawa ko ng order sa shapee pero pur...          1\n",
+            "2  2l gray/black order ko. bakit 850ml lang po pi...          1\n",
+            "3  walang silbing product.. bwesit. di gumagana d...          1\n",
+            "4  d po maganda naman po yung neck fan, pero po n...          4\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load the dataset\n",
+        "df = pd.read_csv('SentiTaglish_ProductsAndServices.csv')\n",
+        "print(\"Original dataset:\")\n",
+        "print(df.head())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "04ac2df1",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "04ac2df1",
+        "outputId": "fe023ce4-54a3-4da7-f37b-b0b3756f4578"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "                                              review\n",
+            "0  at first gumagana cya..pero pagnalowbat cya nd...\n",
+            "1  grabi pangalawa ko ng order sa shapee pero pur...\n",
+            "2  2l gray/black order ko. bakit 850ml lang po pi...\n",
+            "3  walang silbing product.. bwesit. di gumagana d...\n",
+            "4  d po maganda naman po yung neck fan, pero po n...\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Drop the sentiment column\n",
+        "reviews_df = df.drop(columns=['sentiment'])\n",
+        "print(reviews_df.head())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "4ec261ac",
+      "metadata": {
+        "id": "4ec261ac"
+      },
+      "outputs": [],
+      "source": [
+        "documents = reviews_df['review'].astype(str).tolist()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "001e98d7",
+      "metadata": {
+        "id": "001e98d7"
+      },
+      "outputs": [],
+      "source": [
+        "# Load tagalog stopwords function\n",
+        "def load_stopwords(filepath):\n",
+        "    with open(filepath, 'r', encoding='utf-8') as file:\n",
+        "        return set(line.strip() for line in file if line.strip())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "01e67728",
+      "metadata": {
+        "id": "01e67728"
+      },
+      "outputs": [],
+      "source": [
+        "# Define stopwords\n",
+        "english_stopwords = stopwords.words('english')\n",
+        "\n",
+        "# Tagalog/Filipino stopwords\n",
+        "tagalog_stopwords = load_stopwords(\"stopwords-new.txt\")\n",
+        "\n",
+        "combined_stopwords = set(english_stopwords).union(tagalog_stopwords)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "215e29fa",
+      "metadata": {
+        "id": "215e29fa"
+      },
+      "outputs": [],
+      "source": [
+        "# Preprocessing function\n",
+        "def preprocess_data(documents):\n",
+        "    return [\n",
+        "        [word for word in simple_preprocess(str(doc)) if word not in combined_stopwords]\n",
+        "        for doc in documents\n",
+        "    ]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "b3c0885c",
+      "metadata": {
+        "id": "b3c0885c"
+      },
+      "outputs": [],
+      "source": [
+        "# Preprocess the documents\n",
+        "processed_texts = preprocess_data(documents)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "3446b851",
+      "metadata": {
+        "id": "3446b851"
+      },
+      "outputs": [],
+      "source": [
+        "# Create bigram and trigram models\n",
+        "bigram = Phrases(processed_texts, min_count=3, threshold=5)\n",
+        "trigram = Phrases(bigram[processed_texts], threshold=5)\n",
+        "\n",
+        "bigram_mod = Phraser(bigram)\n",
+        "trigram_mod = Phraser(trigram)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "06329215",
+      "metadata": {
+        "id": "06329215"
+      },
+      "outputs": [],
+      "source": [
+        "# Apply phrase models\n",
+        "def make_ngrams(texts):\n",
+        "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
+        "\n",
+        "processed_texts = make_ngrams(processed_texts)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "0bfd68f1",
+      "metadata": {
+        "id": "0bfd68f1"
+      },
+      "outputs": [],
+      "source": [
+        "# Create dictionary and corpus\n",
+        "id2word = corpora.Dictionary(processed_texts)\n",
+        "corpus = [id2word.doc2bow(text) for text in processed_texts]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "a38676bf",
+      "metadata": {
+        "id": "a38676bf"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to train and evaluate an LDA model\n",
+        "def train_evaluate_lda(corpus, id2word, texts, num_topics=10, passes=10, iterations=100, alpha='auto', random_state=42):\n",
+        "    lda = LdaModel(\n",
+        "        corpus=corpus,\n",
+        "        id2word=id2word,\n",
+        "        num_topics=num_topics,\n",
+        "        random_state=random_state,\n",
+        "        passes=passes,\n",
+        "        iterations=iterations,\n",
+        "        alpha=alpha,\n",
+        "        per_word_topics=True\n",
+        "    )\n",
+        "\n",
+        "    coherence_model = CoherenceModel(\n",
+        "        model=lda,\n",
+        "        texts=texts,\n",
+        "        dictionary=id2word,\n",
+        "        coherence='c_v'\n",
+        "    )\n",
+        "    coherence = coherence_model.get_coherence()\n",
+        "\n",
+        "    return lda, coherence"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "195f2b20",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "195f2b20",
+        "outputId": "0d942e49-4be4-484d-e0b5-fe9290740e27"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training LDA models with different configurations...\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# List of parameter configurations to test\n",
+        "configs = [\n",
+        "    {'num_topics': 10, 'passes': 20, 'iterations': 200},\n",
+        "    {'num_topics': 12, 'passes': 50, 'iterations': 500},\n",
+        "    {'num_topics': 15, 'passes': 30, 'iterations': 300},\n",
+        "    {'num_topics': 8, 'passes': 25, 'iterations': 400},\n",
+        "]\n",
+        "\n",
+        "results = []\n",
+        "\n",
+        "print(\"Training LDA models with different configurations...\\n\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "062303dc",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "062303dc",
+        "outputId": "082290b5-6597-4e66-d3da-c567ba6b7cfb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training config: {'num_topics': 10, 'passes': 20, 'iterations': 200}\n",
+            "Coherence Score: 0.5389\n",
+            "\n",
+            "Training config: {'num_topics': 12, 'passes': 50, 'iterations': 500}\n",
+            "Coherence Score: 0.5201\n",
+            "\n",
+            "Training config: {'num_topics': 15, 'passes': 30, 'iterations': 300}\n",
+            "Coherence Score: 0.4666\n",
+            "\n",
+            "Training config: {'num_topics': 8, 'passes': 25, 'iterations': 400}\n",
+            "Coherence Score: 0.4664\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Train and evaluate models\n",
+        "for config in configs:\n",
+        "    print(f\"Training config: {config}\")\n",
+        "    lda_model, coherence = train_evaluate_lda(\n",
+        "        corpus=corpus,\n",
+        "        id2word=id2word,\n",
+        "        texts=processed_texts,\n",
+        "        **config\n",
+        "    )\n",
+        "    results.append({\n",
+        "        'config': config,\n",
+        "        'model': lda_model,\n",
+        "        'coherence': coherence\n",
+        "    })\n",
+        "    print(f\"Coherence Score: {coherence:.4f}\\n\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "_i8DKCwmlJDW",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 861
+        },
+        "id": "_i8DKCwmlJDW",
+        "outputId": "4b636460-b699-4297-bfe4-f3b069a75960"
+      },
+      "outputs": [],
+      "source": [
+        "# Sort results by coherence score\n",
+        "results = sorted(results, key=lambda x: x['coherence'], reverse=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "26e4c265",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "26e4c265",
+        "outputId": "735af3a8-d978-487c-d760-4dcb74175b23"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model comparison:\n",
+            "Config: {'num_topics': 10, 'passes': 20, 'iterations': 200} → Coherence: 0.5389\n",
+            "Config: {'num_topics': 12, 'passes': 50, 'iterations': 500} → Coherence: 0.5201\n",
+            "Config: {'num_topics': 15, 'passes': 30, 'iterations': 300} → Coherence: 0.4666\n",
+            "Config: {'num_topics': 8, 'passes': 25, 'iterations': 400} → Coherence: 0.4664\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print summary\n",
+        "print(\"Model comparison:\")\n",
+        "for res in results:\n",
+        "    print(f\"Config: {res['config']} → Coherence: {res['coherence']:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "0adbf8d2",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Best Model Topics:\n",
+            "[(0,\n",
+            "  '0.010*\"xxl\" + 0.009*\"free\" + 0.008*\"mganda\" + 0.008*\"si\" + '\n",
+            "  '0.008*\"thank_thank\" + 0.008*\"tho\" + 0.007*\"zipper\" + 0.007*\"saktong_sakto\" '\n",
+            "  '+ 0.007*\"hrs\" + 0.006*\"nag_deliver\"'),\n",
+            " (1,\n",
+            "  '0.017*\"gumagana\" + 0.011*\"working\" + 0.009*\"gamitin\" + 0.007*\"need\" + '\n",
+            "  '0.007*\"battery\" + 0.007*\"since\" + 0.006*\"alam\" + 0.006*\"complete\" + '\n",
+            "  '0.006*\"bubble_wrap\" + 0.006*\"good_condition\"'),\n",
+            " (2,\n",
+            "  '0.012*\"well_packaged\" + 0.009*\"kay\" + 0.008*\"promise\" + 0.007*\"magaan\" + '\n",
+            "  '0.007*\"super_cute\" + 0.007*\"guys\" + 0.007*\"ok_price\" + '\n",
+            "  '0.007*\"excellent_quality\" + 0.007*\"wow\" + 0.007*\"sakto_size\"'),\n",
+            " (3,\n",
+            "  '0.018*\"sulit\" + 0.009*\"sna\" + 0.008*\"always\" + 0.007*\"medyo_matagal\" + '\n",
+            "  '0.007*\"although\" + 0.006*\"recieved\" + 0.006*\"masarap\" + 0.006*\"yet\" + '\n",
+            "  '0.006*\"dents\" + 0.005*\"labas\"'),\n",
+            " (4,\n",
+            "  '0.023*\"order_ulit\" + 0.015*\"kuya_rider\" + 0.011*\"well_packed\" + '\n",
+            "  '0.011*\"sobrang_ganda\" + 0.010*\"try\" + 0.010*\"nagdeliver\" + 0.009*\"malambot\" '\n",
+            "  '+ 0.009*\"cya\" + 0.009*\"shoes\" + 0.009*\"ok_lng\"'),\n",
+            " (5,\n",
+            "  '0.030*\"salamat_seller\" + 0.022*\"thank_much\" + 0.020*\"amoy\" + 0.013*\"large\" '\n",
+            "  '+ 0.012*\"maganda_tela\" + 0.011*\"sana_tumagal\" + 0.011*\"mabango\" + '\n",
+            "  '0.007*\"place\" + 0.007*\"long_lasting\" + 0.007*\"great\"'),\n",
+            " (6,\n",
+            "  '0.018*\"ganda_quality\" + 0.016*\"makapal\" + 0.011*\"ok_quality\" + '\n",
+            "  '0.010*\"complete_orders\" + 0.010*\"medyo_manipis\" + 0.010*\"perfect\" + '\n",
+            "  '0.010*\"super_nice\" + 0.009*\"good_price\" + 0.009*\"makapal_tela\" + '\n",
+            "  '0.009*\"okay_price\"'),\n",
+            " (7,\n",
+            "  '0.021*\"uulitin\" + 0.021*\"thankyou_seller\" + 0.016*\"oks\" + '\n",
+            "  '0.011*\"sulit_price\" + 0.010*\"ring_light\" + 0.010*\"arrived\" + 0.008*\"yupi\" + '\n",
+            "  '0.008*\"crack\" + 0.007*\"medjo\" + 0.007*\"hays\"'),\n",
+            " (8,\n",
+            "  '0.012*\"really\" + 0.012*\"pen\" + 0.011*\"food\" + 0.011*\"maraming_salamat\" + '\n",
+            "  '0.009*\"best\" + 0.008*\"tnx\" + 0.007*\"second_order\" + 0.007*\"hotel\" + '\n",
+            "  '0.007*\"bulsa\" + 0.006*\"last\"'),\n",
+            " (9,\n",
+            "  '0.027*\"maganda\" + 0.026*\"order\" + 0.018*\"item\" + 0.017*\"kasi\" + '\n",
+            "  '0.017*\"good\" + 0.017*\"okay\" + 0.017*\"ok\" + 0.016*\"seller\" + 0.015*\"ganda\" + '\n",
+            "  '0.014*\"sana\"')]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Visualize best model\n",
+        "best_model = results[0]['model']\n",
+        "print(\"\\nBest Model Topics:\")\n",
+        "pprint(best_model.print_topics())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "5b434a88",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
+              "\n",
+              "\n",
+              "<div id=\"ldavis_el2825624247404309286068499032\" style=\"background-color:white;\"></div>\n",
+              "<script type=\"text/javascript\">\n",
+              "\n",
+              "var ldavis_el2825624247404309286068499032_data = {\"mdsDat\": {\"x\": [-0.4201629459987754, -0.16424993597475449, 0.04558351207452414, 0.07188331379122327, 0.07517048321200183, 0.07061761738415291, 0.07671511267851532, 0.08130898279615782, 0.0860793212018927, 0.07705453883506265], \"y\": [0.16221486131328683, -0.3433748011333551, 0.07421850542607461, 0.03383169718930712, 0.021486115529566998, 0.00041841368894632823, -0.0015449193843823492, 0.00444409565807013, 0.03685584449042052, 0.011450187222065833], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [51.662640896822985, 14.566464446464161, 5.142288257818122, 5.076741291226168, 4.873073097097158, 4.135216059401928, 4.045097338670364, 3.709387790700807, 3.612349876059305, 3.176740945738997]}, \"tinfo\": {\"Term\": [\"maganda\", \"order\", \"thank_seller\", \"item\", \"ok\", \"seller\", \"good\", \"okay\", \"ganda\", \"gumagana\", \"sana\", \"dumating\", \"salamat_seller\", \"thank\", \"kasi\", \"talaga\", \"order_ulit\", \"thank_much\", \"size\", \"amoy\", \"lng\", \"working\", \"sulit\", \"items\", \"uulitin\", \"kuya_rider\", \"gamitin\", \"thankyou_seller\", \"food\", \"ganda_quality\", \"order\", \"maganda\", \"ok\", \"seller\", \"ganda\", \"dumating\", \"sana\", \"item\", \"size\", \"talaga\", \"items\", \"quality\", \"mura\", \"goods\", \"color\", \"damage\", \"inorder\", \"kulay\", \"price\", \"good_quality\", \"maliit\", \"maayos\", \"thanks_seller\", \"shop\", \"cute\", \"nagustuhan\", \"sakto\", \"thanks\", \"worth\", \"maganda_quality\", \"good\", \"thank\", \"okay\", \"salamat\", \"lng\", \"nice\", \"kasi\", \"thank_seller\", \"product\", \"nung\", \"nag\", \"parang\", \"wala\", \"sira\", \"gumagana\", \"alam\", \"complete\", \"working\", \"good_condition\", \"gamit\", \"battery\", \"fan\", \"nabili\", \"loob\", \"phone\", \"ganon\", \"charge\", \"overall\", \"charger\", \"sound\", \"okey\", \"basag\", \"excellent\", \"gamitin\", \"tubig\", \"nasira\", \"nung_dumating\", \"pack\", \"fast\", \"remote\", \"problem\", \"anyway\", \"though\", \"part\", \"hahaha\", \"room\", \"days\", \"gumana\", \"bubble_wrap\", \"dumi\", \"service\", \"need\", \"nagana\", \"since\", \"masyadong\", \"products\", \"one\", \"first\", \"packaging\", \"order_ulit\", \"kuya_rider\", \"well_packed\", \"sobrang_ganda\", \"nagdeliver\", \"malambot\", \"shoes\", \"gaganda\", \"hehehe\", \"lagi\", \"comfy\", \"suotin\", \"sakto_lng\", \"freebies\", \"kya\", \"sakto_price\", \"thank_kay\", \"looks\", \"complete_items\", \"lakas\", \"gandaaa\", \"next\", \"salamat_kay\", \"medyo_masikip\", \"mahirap\", \"pangalawang_order\", \"nagustuhan_anak\", \"good_job\", \"far_good\", \"head\", \"cya\", \"delivered\", \"ok_lng\", \"maluwag\", \"pro\", \"try\", \"mabait\", \"payong\", \"thank_seller\", \"sulit\", \"sna\", \"always\", \"medyo_matagal\", \"recieved\", \"masarap\", \"yet\", \"dents\", \"labas\", \"pede\", \"nagkamali\", \"stretchable\", \"maling_kulay\", \"suki\", \"related\", \"ice\", \"silang\", \"shipment\", \"ha\", \"ginagamit\", \"husband\", \"nasunod_color\", \"pares\", \"rice\", \"accurate\", \"sana_susunod\", \"feedback\", \"padding\", \"maliit_lng\", \"serving\", \"mins\", \"perfume\", \"yes\", \"correct\", \"madali\", \"although\", \"nakuha\", \"idk\", \"khit\", \"try\", \"salamat_seller\", \"thank_much\", \"sana_tumagal\", \"mabango\", \"amoy\", \"day\", \"nagustohan\", \"long_lasting\", \"scent\", \"medium\", \"keri\", \"complete_order\", \"medj\", \"thank_seller_rider\", \"secure\", \"plastik\", \"order_uli\", \"sulit_sulit\", \"large\", \"kso\", \"clip\", \"different\", \"super_bilis\", \"matanggal\", \"mabilis_deliver\", \"super_tagal\", \"ok_xa\", \"really_love\", \"cloud\", \"solid\", \"maganda_tela\", \"place\", \"great\", \"kompleto\", \"bottle\", \"xxl\", \"skin\", \"original\", \"well_packaged\", \"kay\", \"promise\", \"magaan\", \"super_cute\", \"ok_price\", \"excellent_quality\", \"wow\", \"sakto_size\", \"umabot\", \"quality_good\", \"magaganda\", \"feeling\", \"guys\", \"fake_nails\", \"shipped\", \"fast_shipping\", \"come\", \"bukas\", \"jnt\", \"nasa_pic\", \"lol\", \"durable\", \"brush\", \"immediately\", \"sha\", \"air\", \"tag\", \"shade\", \"helpful\", \"beses\", \"nong\", \"effective\", \"glue\", \"pen\", \"maraming_salamat\", \"tnx\", \"second_order\", \"hotel\", \"bulsa\", \"sarap\", \"freebie\", \"ever\", \"finally\", \"last\", \"mahaba\", \"today\", \"kahapon\", \"worst\", \"choice\", \"tamang_tama\", \"hahahahaha\", \"syempre\", \"works\", \"damages\", \"thank_shoppee\", \"marunong\", \"magtatagal\", \"gnda\", \"cr\", \"enough\", \"normal\", \"kids\", \"malinis\", \"per\", \"really\", \"mali_kulay\", \"best\", \"food\", \"staff\", \"even\", \"mganda\", \"si\", \"thank_thank\", \"tho\", \"zipper\", \"saktong_sakto\", \"hrs\", \"nag_deliver\", \"stylus\", \"kuya\", \"tape\", \"nails\", \"tama_size\", \"slipper\", \"accommodating\", \"isuot\", \"yellow\", \"madumi\", \"connect\", \"mabilis_malowbat\", \"times\", \"kumain\", \"months\", \"little\", \"natanggal\", \"icharge\", \"makuha\", \"wear\", \"ayaw_gumana\", \"pocket\", \"free\", \"side\", \"xxl\", \"mabilis_shipping\", \"wire\", \"hassle\", \"sizing\", \"ganda_quality\", \"ok_quality\", \"complete_orders\", \"medyo_manipis\", \"super_nice\", \"perfect\", \"good_price\", \"makapal_tela\", \"okay_price\", \"cotton\", \"foam\", \"sia\", \"asawa\", \"ganda_tela\", \"highly_recommended\", \"seller_shopee\", \"napakaganda\", \"tingnan\", \"goods_quality\", \"surely_buy\", \"ilang_beses\", \"cm\", \"hopefully\", \"luma\", \"uli\", \"fabric\", \"lakad\", \"thank_seller_shopee\", \"bed\", \"ibang_item\", \"makapal\", \"pra\", \"tao\", \"sizing\", \"kona\", \"uulitin\", \"thankyou_seller\", \"sulit_price\", \"ring_light\", \"arrived\", \"yupi\", \"crack\", \"medjo\", \"hays\", \"mouse\", \"pics\", \"toh\", \"tama_kulay\", \"hubby\", \"sobrang_bilis\", \"bluetooth\", \"thank_parin\", \"straps\", \"fragile\", \"date\", \"nabasag\", \"mura_lng\", \"bebe\", \"naming\", \"nakakatuwa\", \"compatible\", \"karton\", \"yupi_yupi\", \"poor_service\", \"dent\", \"oks\", \"mejo\", \"laging\", \"gift\"], \"Freq\": [1783.0, 1722.0, 950.0, 1151.0, 1085.0, 1029.0, 1104.0, 1096.0, 992.0, 315.0, 928.0, 899.0, 183.0, 892.0, 1180.0, 771.0, 151.0, 136.0, 661.0, 126.0, 685.0, 197.0, 116.0, 545.0, 87.0, 100.0, 163.0, 83.0, 118.0, 81.0, 1721.2888620790675, 1782.8052592370436, 1084.515218575957, 1028.8335085347467, 991.6366303476947, 899.2044740063195, 927.4275401637769, 1149.7954888937747, 660.7189484077664, 770.4896084244444, 545.2587694716447, 449.246184223142, 393.418695521206, 361.919913264415, 360.0615405718645, 351.38645880871087, 341.99744009531156, 334.4701821463514, 329.3185425316204, 315.38660778616423, 301.517276062724, 383.1514641557132, 287.26280297809535, 254.2433563835002, 256.4168502848722, 250.38777635888698, 255.9824903117867, 248.31705397147294, 248.43043228706452, 247.47693423574992, 1098.4223074608958, 889.6974832402182, 1090.456858880994, 380.5846486835474, 668.8681464720837, 335.3337131535938, 1126.2567526157657, 901.5223425025894, 563.6628980638769, 415.5526639385415, 391.69444163837676, 437.7678545116959, 421.22118291690913, 342.89207421295583, 315.14312237190745, 115.2792317295971, 112.3696760805386, 195.9243894719056, 106.19414249418872, 105.01253139308814, 125.1271842868342, 91.98198555510173, 90.39884751255984, 89.86148163842587, 84.81444220610491, 76.9094321831745, 71.7855315301314, 67.85406416887952, 65.95684162506512, 65.30900764778599, 63.370122897106064, 93.04515928977322, 62.63190318803494, 161.92734018037928, 62.08019091876674, 60.24749272859294, 59.773097804187316, 55.14774083664561, 61.27566539007253, 54.73995724897601, 54.63432147988476, 54.33075365218994, 83.8207112202865, 86.74036184324571, 94.06037974688748, 87.58055111605285, 99.87768376067447, 68.17887619356826, 106.93985382711368, 92.70684426930428, 93.56649197006493, 132.73816636149766, 72.87951515957228, 123.54987258472775, 76.18215515041601, 96.50368185714144, 101.22071977691168, 83.2252540641153, 94.1450983513874, 150.9401881984625, 99.77641955166813, 71.82070275169018, 69.77465949867606, 63.82639599110575, 61.58066110406666, 60.09780610541103, 53.203820664683484, 50.88900428460551, 50.43105760465403, 38.819399844210295, 36.85708805166549, 36.01232086088617, 34.61130455419943, 35.48296242555256, 33.836922223532326, 33.77580372606275, 32.66384628524804, 31.779762373498706, 31.012668568726593, 28.560539885042967, 28.092029551678866, 27.625336033023057, 25.357000335590957, 25.85498681661375, 24.259234101302095, 24.16100761913831, 23.796048128767907, 23.299298175481365, 22.826165294326838, 60.78212546749494, 37.73900879941432, 59.390457553546376, 27.729289431115166, 31.376311020341067, 66.4635663413839, 34.38270794503276, 42.17791854932271, 48.02897335554409, 115.44042181871826, 57.397760679081856, 50.74276753343991, 44.838710535775895, 38.61375101135096, 38.27633364823134, 36.68740902833091, 36.51650776678959, 34.667281011299934, 28.910246849414136, 28.886320432844318, 28.019825421736712, 26.93514920620323, 26.896279652194423, 26.58637301902069, 25.777766918505726, 24.770237424469556, 24.006250776492216, 23.860468203124885, 23.567254872122312, 22.806563092968084, 22.606081497941542, 22.026719982511576, 20.96568673784579, 20.880018648732225, 19.896791999155997, 19.277190192300594, 18.974104252530367, 18.811935818097357, 18.605414879989038, 34.08477759774324, 27.557652957151817, 25.455193711786258, 27.401772818110384, 33.3184903904693, 44.12541117795944, 33.72798094698947, 23.28811334547442, 22.988569727124396, 23.365498521292565, 183.2358755070294, 136.04897475080762, 70.07542781181874, 65.60679633037144, 124.82058574912021, 40.090484925936025, 39.5115968562234, 43.19620764444662, 36.38766962146046, 34.79698991280889, 32.203651053839664, 32.06499672864649, 30.337197252315985, 27.50091749727074, 26.69705984105779, 25.791875323389068, 24.512483341639754, 24.168630173755336, 82.22024995445463, 23.649706450436135, 23.62106377608276, 23.06425116554534, 22.813543911318018, 21.995316693090924, 21.69382652570262, 21.6004992290748, 21.07820408577592, 20.784380947769236, 20.621476007299542, 20.497974357898883, 76.12195294202886, 46.11187555877417, 43.096363516799165, 29.68100260884224, 25.852166447039274, 37.821303347506245, 24.31493713285626, 23.697934166985707, 60.392183636151344, 47.30934094722622, 41.09913585102084, 37.78617445635865, 37.25757561549772, 36.22823235806445, 36.1046550723052, 34.56077822448284, 34.303782619630894, 30.525031044717473, 29.718713747779635, 28.371728418156845, 25.43067648207995, 36.48246475320932, 24.43195574495483, 24.014249322656738, 23.826182484794888, 22.832743851459956, 22.474032322881417, 21.855261471526376, 20.991187961082503, 20.825314754501907, 20.360545813558858, 19.954488360211556, 19.41044022086901, 19.197954036391558, 19.145519017175545, 19.419475012643346, 18.96061484506596, 18.92175888823009, 19.605415082998555, 23.231469392931977, 27.75063168206163, 24.322861489384398, 59.24105218737073, 56.23641587699787, 38.966993747588816, 35.292570102501905, 35.207611100868576, 35.098767313099955, 29.856006568239735, 28.355186888229092, 27.20113635109141, 26.872377589278642, 31.78412110495589, 26.52024070279353, 26.107789593004796, 24.90807260665027, 23.254090600338603, 22.379815177162087, 21.419541140388176, 21.04064679286937, 20.17374186003118, 18.478094118129892, 18.432989298364898, 17.816479089418408, 17.5186230919765, 17.163223974845533, 17.05481754107559, 16.958578152154033, 16.601796470606487, 16.159763023136886, 16.06864282692113, 15.787368042827897, 16.154271004071557, 62.620355089941874, 23.963084450616936, 47.25073659305059, 57.57552690338912, 27.010089130872373, 23.073389311596422, 38.70422420395328, 37.914027475285415, 36.86217486544007, 36.78085009963721, 35.14545481931576, 33.03750452925317, 33.008479381083156, 30.463381080209885, 30.32799248322264, 29.432964212908402, 27.978638606926637, 25.736746155832122, 24.259034310336446, 23.719575062953048, 23.599301721698858, 22.89485672948496, 22.32617509266491, 20.09645681086098, 20.069905944802407, 17.71827174030715, 17.519545685249263, 16.700403800702848, 15.578030863326939, 15.496924158702202, 15.352415344702761, 15.121582794576112, 15.050975900045193, 15.004892669665775, 14.616084110167838, 14.249491492001782, 40.34743681314234, 24.457143438543692, 44.9127481055401, 18.649985790430506, 21.612063086816317, 18.646604198492547, 18.59326796842507, 81.22078225264751, 48.74012614373963, 44.95522583806225, 44.81011472981738, 44.278215673766624, 44.40775965023808, 43.380294805385084, 39.63369553436711, 39.36884513889771, 32.33309276123229, 32.275846096088564, 29.44928053308805, 28.518695152533198, 27.970686868263943, 26.267498804612906, 25.774391657955295, 24.96717590341666, 25.887890433347433, 22.452403300096716, 21.580001515155633, 20.89386321505045, 20.466070153073655, 20.287671335167428, 19.630682457294398, 18.8107018254406, 18.137842754002023, 17.27655608042315, 17.093480079999583, 16.23017228306049, 16.204348003589924, 71.06962935017414, 34.939808336419354, 18.646585328048175, 18.54018075967754, 18.346628852164613, 86.5953590495417, 83.08289674568844, 44.73754500081431, 40.756558944761736, 40.151980684703084, 31.427949425017605, 31.320063023848192, 29.90901505247672, 29.811338839233294, 29.372325157761978, 28.946901912094354, 28.4393185604598, 28.395097560878146, 21.04389363526179, 19.767481255927596, 19.429358870772724, 18.25370828353312, 18.097712002380828, 17.450783984902692, 17.21698194263426, 16.767136828346608, 16.322824735015907, 15.93435336065101, 15.81370234770133, 15.025810861606416, 14.974547835352281, 14.66729906900045, 14.536819811735617, 14.048544788830583, 13.754122571060773, 65.30934694872103, 22.823880365971362, 16.13380863103606, 14.97614786939767], \"Total\": [1783.0, 1722.0, 950.0, 1151.0, 1085.0, 1029.0, 1104.0, 1096.0, 992.0, 315.0, 928.0, 899.0, 183.0, 892.0, 1180.0, 771.0, 151.0, 136.0, 661.0, 126.0, 685.0, 197.0, 116.0, 545.0, 87.0, 100.0, 163.0, 83.0, 118.0, 81.0, 1722.0288803322912, 1783.6276408665988, 1085.2574009779653, 1029.574207694155, 992.3948474456311, 899.9444935246147, 928.2946781267248, 1151.0444900247455, 661.458949747167, 771.3757918458532, 545.9998013452094, 450.0053158389154, 394.1587506539354, 362.659956259669, 360.8016199828286, 352.1297785451194, 342.75253718516655, 335.21017330451076, 330.0628627160556, 316.126685090031, 302.25730023643416, 384.1109820401917, 288.0031141288079, 254.98377029792246, 257.1657682155553, 251.12797156398713, 256.7416325921119, 249.05728560708673, 249.17142101771478, 248.21699198568172, 1104.1134718035028, 892.4517217872655, 1096.1449067899828, 383.05162750265276, 685.2318807949789, 338.46101132062535, 1180.5073227731266, 950.2119775748306, 621.1195229677792, 438.28385797283687, 415.78486230198064, 515.3569048429214, 489.9414245496452, 355.3520885805172, 315.88432461572734, 116.02201483268922, 113.10967130208682, 197.2392612348468, 106.93405233146696, 105.75241809326049, 126.10623871243497, 92.72186341236515, 91.13896507461855, 90.60706477318435, 85.6647899539156, 77.69506565743983, 72.52679773895338, 68.59401091105056, 66.6969903930454, 66.04886790908492, 64.11002644555248, 94.13462740610332, 63.371807160565744, 163.85390885800274, 62.82011233059417, 60.98735516607398, 60.520806259265214, 55.887677549899436, 62.09863975668097, 55.47981523080489, 55.374228561059006, 55.0706612178702, 84.9783613739369, 87.93939442752807, 95.6252489275745, 88.98900743750616, 102.5567524612627, 69.55386083259852, 112.74202987210899, 96.96047509144046, 98.33868631217666, 146.06324636518892, 75.06716816649453, 137.83868917351742, 79.49363790496847, 124.70134222915279, 142.48705493503644, 100.52273001579672, 246.26510937195332, 151.7054874633346, 100.54170726593807, 72.58604676447202, 70.53995851229482, 64.59172945700045, 62.345954783284306, 60.863110606345295, 53.96912413906372, 51.65431312972489, 51.19637347934068, 39.58470565864178, 37.6223843366923, 36.77762466018435, 35.37663741605173, 36.28230167725583, 34.60219327508745, 34.541088150810495, 33.42923600723123, 32.545055201155186, 31.777989041321618, 29.325820121046757, 28.857314096561648, 28.390607578107936, 26.122342507786552, 26.638683126290683, 25.024555737229832, 24.926335029627246, 24.5613517917864, 24.064686852126123, 23.591511924407786, 63.1334896850277, 39.42098038059976, 69.17676089181501, 29.30812914760913, 34.101688748768936, 92.33965658827458, 41.858351006938236, 60.98807881969175, 950.2119775748306, 116.19955066714249, 58.15678870897639, 51.5018161880134, 45.5977851705355, 39.37287336871932, 39.035404738076835, 37.44681652587859, 37.27553512352577, 35.42639923165583, 29.669330504189002, 29.645489972935508, 28.778861063027176, 27.69415893659751, 27.655356321134597, 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\"jnt\", \"kahapon\", \"karton\", \"kasi\", \"kasi\", \"kay\", \"keri\", \"khit\", \"khit\", \"kids\", \"kompleto\", \"kompleto\", \"kona\", \"kona\", \"kona\", \"kona\", \"kso\", \"kulay\", \"kumain\", \"kuya\", \"kuya_rider\", \"kya\", \"labas\", \"lagi\", \"laging\", \"laging\", \"lakad\", \"lakas\", \"large\", \"large\", \"last\", \"little\", \"lng\", \"lng\", \"lng\", \"lol\", \"long_lasting\", \"loob\", \"looks\", \"luma\", \"maayos\", \"mabait\", \"mabait\", \"mabango\", \"mabilis_deliver\", \"mabilis_malowbat\", \"mabilis_shipping\", \"mabilis_shipping\", \"madali\", \"madali\", \"madali\", \"madumi\", \"magaan\", \"magaganda\", \"maganda\", \"maganda_quality\", \"maganda_tela\", \"maganda_tela\", \"maganda_tela\", \"magtatagal\", \"mahaba\", \"mahirap\", \"makapal\", \"makapal\", \"makapal_tela\", \"makuha\", \"malambot\", \"mali_kulay\", \"mali_kulay\", \"maliit\", \"maliit_lng\", \"maling_kulay\", \"malinis\", \"maluwag\", \"maluwag\", \"maraming_salamat\", \"marunong\", \"masarap\", \"masyadong\", \"masyadong\", \"matanggal\", \"medium\", \"medj\", \"medjo\", \"medyo_manipis\", \"medyo_masikip\", \"medyo_matagal\", \"mejo\", \"mejo\", \"mganda\", \"mins\", \"mins\", \"months\", \"mouse\", \"mura\", \"mura_lng\", \"nabasag\", \"nabili\", \"nag\", \"nag\", \"nag\", \"nag_deliver\", \"nagana\", \"nagana\", \"nagdeliver\", \"nagkamali\", \"nagustohan\", \"nagustuhan\", \"nagustuhan_anak\", \"nails\", \"nakakatuwa\", \"nakuha\", \"nakuha\", \"nakuha\", \"naming\", \"napakaganda\", \"nasa_pic\", \"nasira\", \"nasunod_color\", \"natanggal\", \"need\", \"need\", \"next\", \"nice\", \"nice\", \"nong\", \"nong\", \"normal\", \"nung\", \"nung\", \"nung_dumating\", \"ok\", \"ok_lng\", \"ok_lng\", \"ok_price\", \"ok_quality\", \"ok_xa\", \"okay\", \"okay\", \"okay_price\", \"okey\", \"oks\", \"oks\", \"one\", \"one\", \"order\", \"order_uli\", \"order_ulit\", \"original\", \"original\", \"overall\", \"pack\", \"packaging\", \"packaging\", \"padding\", \"pangalawang_order\", \"parang\", \"parang\", \"parang\", \"pares\", \"part\", \"part\", \"payong\", \"payong\", \"payong\", \"pede\", \"pen\", \"per\", \"perfect\", \"perfume\", \"perfume\", \"phone\", \"pics\", \"place\", \"place\", \"plastik\", \"pocket\", \"poor_service\", \"pra\", \"pra\", \"price\", \"pro\", \"pro\", \"problem\", \"product\", \"product\", \"products\", \"products\", \"products\", \"products\", \"products\", \"promise\", \"quality\", \"quality_good\", \"really\", \"really\", \"really_love\", \"recieved\", \"related\", \"remote\", \"rice\", \"ring_light\", \"room\", \"room\", \"sakto\", \"sakto_lng\", \"sakto_price\", \"sakto_size\", \"saktong_sakto\", \"salamat\", \"salamat\", \"salamat_kay\", \"salamat_seller\", \"sana\", \"sana_susunod\", \"sana_tumagal\", \"sarap\", \"scent\", \"second_order\", \"secure\", \"seller\", \"seller_shopee\", \"service\", \"service\", \"serving\", \"sha\", \"shade\", \"shipment\", \"shipped\", \"shoes\", \"shop\", \"si\", \"sia\", \"side\", \"side\", \"silang\", \"since\", \"since\", \"sira\", \"sira\", \"size\", \"sizing\", \"sizing\", \"skin\", \"skin\", \"skin\", \"slipper\", \"sna\", \"sobrang_bilis\", \"sobrang_ganda\", \"solid\", \"sound\", \"staff\", \"staff\", \"straps\", \"stretchable\", \"stylus\", \"suki\", \"sulit\", \"sulit_price\", \"sulit_sulit\", \"suotin\", \"super_bilis\", \"super_cute\", \"super_nice\", \"super_tagal\", \"surely_buy\", \"syempre\", \"tag\", \"talaga\", \"tama_kulay\", \"tama_size\", \"tamang_tama\", \"tao\", \"tao\", \"tao\", \"tape\", \"thank\", \"thank\", \"thank_kay\", \"thank_much\", \"thank_parin\", \"thank_seller\", \"thank_seller\", \"thank_seller_rider\", \"thank_seller_shopee\", \"thank_shoppee\", \"thank_thank\", \"thanks\", \"thanks_seller\", \"thankyou_seller\", \"tho\", \"though\", \"times\", \"tingnan\", \"tnx\", \"today\", \"toh\", \"try\", \"try\", \"try\", \"tubig\", \"uli\", \"umabot\", \"uulitin\", \"wala\", \"wala\", \"wear\", \"well_packaged\", \"well_packed\", \"wire\", \"wire\", \"working\", \"working\", \"works\", \"worst\", \"worth\", \"wow\", \"xxl\", \"xxl\", \"yellow\", \"yes\", \"yes\", \"yet\", \"yupi\", \"yupi_yupi\", \"zipper\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [10, 2, 5, 4, 6, 3, 9, 1, 7, 8]};\n",
+              "\n",
+              "function LDAvis_load_lib(url, callback){\n",
+              "  var s = document.createElement('script');\n",
+              "  s.src = url;\n",
+              "  s.async = true;\n",
+              "  s.onreadystatechange = s.onload = callback;\n",
+              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+              "}\n",
+              "\n",
+              "if(typeof(LDAvis) !== \"undefined\"){\n",
+              "   // already loaded: just create the visualization\n",
+              "   !function(LDAvis){\n",
+              "       new LDAvis(\"#\" + \"ldavis_el2825624247404309286068499032\", ldavis_el2825624247404309286068499032_data);\n",
+              "   }(LDAvis);\n",
+              "}else if(typeof define === \"function\" && define.amd){\n",
+              "   // require.js is available: use it to load d3/LDAvis\n",
+              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
+              "   require([\"d3\"], function(d3){\n",
+              "      window.d3 = d3;\n",
+              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
+              "        new LDAvis(\"#\" + \"ldavis_el2825624247404309286068499032\", ldavis_el2825624247404309286068499032_data);\n",
+              "      });\n",
+              "    });\n",
+              "}else{\n",
+              "    // require.js not available: dynamically load d3 & LDAvis\n",
+              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
+              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
+              "                 new LDAvis(\"#\" + \"ldavis_el2825624247404309286068499032\", ldavis_el2825624247404309286068499032_data);\n",
+              "            })\n",
+              "         });\n",
+              "}\n",
+              "</script>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Prepare interactive visualization\n",
+        "vis = gensimvis.prepare(best_model, corpus, id2word)\n",
+        "pyLDAvis.enable_notebook()\n",
+        "pyLDAvis.display(vis)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "57bbcaa1",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LDA Topic</th>\n",
+              "      <th>Top Keywords (Partial)</th>\n",
+              "      <th>Matching Open-Coded Aspects</th>\n",
+              "      <th>Notes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Topic 0</td>\n",
+              "      <td>xxl, free, zipper, saktong_sakto, nag_deliver</td>\n",
+              "      <td>PRO#SIZE, DEL#TIME, PRO#MAT</td>\n",
+              "      <td>Mentions of sizing, delivery timing, and product construction</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Topic 1</td>\n",
+              "      <td>gumagana, working, battery, complete, good_condition</td>\n",
+              "      <td>PRO#FUNC, PRO#EFF, PRO#COND</td>\n",
+              "      <td>Functionality and condition of items, esp. electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Topic 2</td>\n",
+              "      <td>well_packaged, magaan, excellent_quality, ok_price</td>\n",
+              "      <td>DEL#COND, PRO#GEN, PRI#VOM</td>\n",
+              "      <td>Packaging, general quality, and pricing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Topic 3</td>\n",
+              "      <td>sulit, recieved, masarap, dents</td>\n",
+              "      <td>PRI#VOM, DEL#COND, PRO#EFF</td>\n",
+              "      <td>Value for money, food quality, and damage</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Topic 4</td>\n",
+              "      <td>order_ulit, sobrang_ganda, malambot, shoes</td>\n",
+              "      <td>SAT#RECO, PRO#MAT, SAT#EMO</td>\n",
+              "      <td>Reorders, satisfaction, and softness of materials</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Topic 5</td>\n",
+              "      <td>salamat_seller, amoy, mabango, sana_tumagal</td>\n",
+              "      <td>SAT#EMO, PRO#DUR, SER#GEN</td>\n",
+              "      <td>Thanks, scent-related comments, and durability</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Topic 6</td>\n",
+              "      <td>ganda_quality, makapal, perfect, good_price</td>\n",
+              "      <td>PRO#GEN, PRO#MAT, PRI#VOM</td>\n",
+              "      <td>General praise, thickness, and pricing satisfaction</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Topic 7</td>\n",
+              "      <td>uulitin, thankyou_seller, sulit_price, yupi</td>\n",
+              "      <td>SAT#RECO, DEL#COND, PRI#VOM</td>\n",
+              "      <td>Repeat order, damage in transit, and value</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Topic 8</td>\n",
+              "      <td>maraming_salamat, second_order, pen, hotel</td>\n",
+              "      <td>SAT#EMO, SAT#RECO, SER#GEN</td>\n",
+              "      <td>Thankfulness, second order = positive emotional feedback</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Topic 9</td>\n",
+              "      <td>maganda, item, order, seller, sana</td>\n",
+              "      <td>PRO#GEN, SER#GEN, SAT#GEN</td>\n",
+              "      <td>Vague praise and seller mentions, low specificity</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  LDA Topic                                Top Keywords (Partial)  \\\n",
+              "0   Topic 0         xxl, free, zipper, saktong_sakto, nag_deliver   \n",
+              "1   Topic 1  gumagana, working, battery, complete, good_condition   \n",
+              "2   Topic 2    well_packaged, magaan, excellent_quality, ok_price   \n",
+              "3   Topic 3                       sulit, recieved, masarap, dents   \n",
+              "4   Topic 4            order_ulit, sobrang_ganda, malambot, shoes   \n",
+              "5   Topic 5           salamat_seller, amoy, mabango, sana_tumagal   \n",
+              "6   Topic 6           ganda_quality, makapal, perfect, good_price   \n",
+              "7   Topic 7           uulitin, thankyou_seller, sulit_price, yupi   \n",
+              "8   Topic 8            maraming_salamat, second_order, pen, hotel   \n",
+              "9   Topic 9                    maganda, item, order, seller, sana   \n",
+              "\n",
+              "   Matching Open-Coded Aspects  \\\n",
+              "0  PRO#SIZE, DEL#TIME, PRO#MAT   \n",
+              "1  PRO#FUNC, PRO#EFF, PRO#COND   \n",
+              "2   DEL#COND, PRO#GEN, PRI#VOM   \n",
+              "3   PRI#VOM, DEL#COND, PRO#EFF   \n",
+              "4   SAT#RECO, PRO#MAT, SAT#EMO   \n",
+              "5    SAT#EMO, PRO#DUR, SER#GEN   \n",
+              "6    PRO#GEN, PRO#MAT, PRI#VOM   \n",
+              "7  SAT#RECO, DEL#COND, PRI#VOM   \n",
+              "8   SAT#EMO, SAT#RECO, SER#GEN   \n",
+              "9    PRO#GEN, SER#GEN, SAT#GEN   \n",
+              "\n",
+              "                                                           Notes  \n",
+              "0  Mentions of sizing, delivery timing, and product construction  \n",
+              "1         Functionality and condition of items, esp. electronics  \n",
+              "2                        Packaging, general quality, and pricing  \n",
+              "3                      Value for money, food quality, and damage  \n",
+              "4              Reorders, satisfaction, and softness of materials  \n",
+              "5                 Thanks, scent-related comments, and durability  \n",
+              "6            General praise, thickness, and pricing satisfaction  \n",
+              "7                     Repeat order, damage in transit, and value  \n",
+              "8       Thankfulness, second order = positive emotional feedback  \n",
+              "9              Vague praise and seller mentions, low specificity  "
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a DataFrame with the comparison\n",
+        "data = {\n",
+        "    \"LDA Topic\": [\n",
+        "        \"Topic 0\", \"Topic 1\", \"Topic 2\", \"Topic 3\", \"Topic 4\",\n",
+        "        \"Topic 5\", \"Topic 6\", \"Topic 7\", \"Topic 8\", \"Topic 9\"\n",
+        "    ],\n",
+        "    \"Top Keywords (Partial)\": [\n",
+        "        'xxl, free, zipper, saktong_sakto, nag_deliver',\n",
+        "        'gumagana, working, battery, complete, good_condition',\n",
+        "        'well_packaged, magaan, excellent_quality, ok_price',\n",
+        "        'sulit, recieved, masarap, dents',\n",
+        "        'order_ulit, sobrang_ganda, malambot, shoes',\n",
+        "        'salamat_seller, amoy, mabango, sana_tumagal',\n",
+        "        'ganda_quality, makapal, perfect, good_price',\n",
+        "        'uulitin, thankyou_seller, sulit_price, yupi',\n",
+        "        'maraming_salamat, second_order, pen, hotel',\n",
+        "        'maganda, item, order, seller, sana'\n",
+        "    ],\n",
+        "    \"Matching Open-Coded Aspects\": [\n",
+        "        \"PRO#SIZE, DEL#TIME, PRO#MAT\",\n",
+        "        \"PRO#FUNC, PRO#EFF, PRO#COND\",\n",
+        "        \"DEL#COND, PRO#GEN, PRI#VOM\",\n",
+        "        \"PRI#VOM, DEL#COND, PRO#EFF\",\n",
+        "        \"SAT#RECO, PRO#MAT, SAT#EMO\",\n",
+        "        \"SAT#EMO, PRO#DUR, SER#GEN\",\n",
+        "        \"PRO#GEN, PRO#MAT, PRI#VOM\",\n",
+        "        \"SAT#RECO, DEL#COND, PRI#VOM\",\n",
+        "        \"SAT#EMO, SAT#RECO, SER#GEN\",\n",
+        "        \"PRO#GEN, SER#GEN, SAT#GEN\"\n",
+        "    ],\n",
+        "    \"Notes\": [\n",
+        "        \"Mentions of sizing, delivery timing, and product construction\",\n",
+        "        \"Functionality and condition of items, esp. electronics\",\n",
+        "        \"Packaging, general quality, and pricing\",\n",
+        "        \"Value for money, food quality, and damage\",\n",
+        "        \"Reorders, satisfaction, and softness of materials\",\n",
+        "        \"Thanks, scent-related comments, and durability\",\n",
+        "        \"General praise, thickness, and pricing satisfaction\",\n",
+        "        \"Repeat order, damage in transit, and value\",\n",
+        "        \"Thankfulness, second order = positive emotional feedback\",\n",
+        "        \"Vague praise and seller mentions, low specificity\"\n",
+        "    ]\n",
+        "}\n",
+        "\n",
+        "df = pd.DataFrame(data)\n",
+        "\n",
+        "# Display the table\n",
+        "pd.set_option('display.max_colwidth', None)\n",
+        "df"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

2 _ Topic Modeling/stopwords-new.txt

@@ -0,0 +1,177 @@
+akin
+aking
+ako
+alin
+am
+amin
+aming
+ang
+ano
+anumang
+apat
+at
+atin
+ating
+ay
+bababa
+bago
+bakit
+bawat
+bilang
+dahil
+dalawa
+dapat
+di
+din
+dito
+doon
+eh
+gagawin
+gayunman
+ginagawa
+ginawa
+ginawang
+gumawa
+gusto
+habang
+hanggang
+hindi
+huwag
+iba
+ibaba
+ibabaw
+ibig
+ikaw
+ilagay
+ilalim
+ilan
+inyong
+isa
+isang
+itaas
+ito
+iyo
+iyon
+iyong
+ka
+kahit
+kailangan
+kailanman
+kami
+kanila
+kanilang
+kanino
+kanya
+kanyang
+kapag
+kapwa
+karamihan
+kase
+kaso
+katiyakan
+katulad
+kaya
+kayo
+kaysa
+kc
+ko
+kong
+kulang
+kumuha
+kung
+laban
+lahat
+lamang
+lang
+likod
+lima
+maaari
+maaaring
+maging
+mahusay
+makita
+marami
+marapat
+masyado
+may
+mayroon
+mga
+minsan
+mismo
+mo
+mula
+muli
+na
+nabanggit
+naging
+nagkaroon
+nais
+nakita
+nalang
+naman
+namin
+nang
+napaka
+narito
+nasaan
+ng
+nga
+ngayon
+ni
+nila
+nilang
+nito
+niya
+niyang
+nman
+nmn
+noon
+nya
+nyo
+o
+pa
+paano
+pababa
+pag
+paggawa
+pagitan
+pagkakaroon
+pagkatapos
+pala
+palabas
+pamamagitan
+panahon
+pang
+pangalawa
+para
+paraan
+pareho
+pataas
+pero
+po
+pumunta
+pumupunta
+rin
+sa
+saan
+sabi
+sabihin
+sakin
+sarili
+sila
+sino
+siya
+sya
+tapos
+tas
+tatlo
+tayo
+tulad
+tungkol
+un
+una
+ung
+walang
+yong
+yun
+yung