Updating Progress

Progress/Similar.py

@@ -1,14 +1,13 @@
 import textdistance as td
 import Cleaner
 
+
 def match(resume, job_des):
-    j = td.jaccard.similarity(resume, job_des)*100
-    s = td.sorensen_dice.similarity(resume, job_des)*100
-    c = td.cosine.similarity(resume, job_des)*100
-    o = td.overlap.normalized_similarity(resume, job_des)*100
+    j = td.jaccard.similarity(resume, job_des)
+    s = td.sorensen_dice.similarity(resume, job_des)
+    c = td.cosine.similarity(resume, job_des)
+    o = td.overlap.normalized_similarity(resume, job_des)
     total = (j+s+c+o)/4
-    return total
+    return total*100
 
-# https://realpython.com/working-with-files-in-python/
 
-# https://support.dlink.ca/emulators/wbr2310/index.htm

Progress/app.py

@@ -0,0 +1,50 @@
+import Cleaner
+import Similar
+import textract as tx
+import pandas as pd
+import os
+import streamlit as st
+
+st.title("Naive Resume Matcher")
+st.markdown(""" ### Ranking **Resumes** based on the Matching Skills as provided by the required job description. This uses a **Token, String and Word Embedding** based algorithm created to generate a match score that ranks a resume.""")
+
+
+resume_dir = "Data/Resumes/"
+job_desc_dir = "Data/JobDesc/"
+resume_names = os.listdir(resume_dir)
+document = []
+
+for res in resume_names:
+    temp = []
+    temp.append(res)
+    text = tx.process(resume_dir+res, encoding='ascii')
+    text = str(text, 'utf-8')
+    temp.append(text)
+    document.append(temp)
+
+df = pd.DataFrame(document, columns=['Name', 'Context'])
+
+# Only one Job Description should be present and in docx format
+job_docs = os.listdir(job_desc_dir)
+job_desc = tx.process(
+    job_desc_dir+job_docs[1], extension='docx', encoding='ascii')
+job_desc = str(job_desc, 'utf-8')
+job_des = Cleaner.Cleaner(job_desc)
+
+st.subheader("Job Description")
+st.markdown(" --- ")
+st.write(job_desc)
+st.markdown(" --- ")
+
+scores = []
+for text in df['Context']:
+    raw = Cleaner.Cleaner(text)
+    score = Similar.match(raw[2], job_des[2])
+    scores.append(score)
+st.write(scores)
+df['Scores'] = scores
+
+st.dataframe(df)
+df2 = df.sort_values(by=['Scores'], ascending=False)
+st.dataframe(df2)
+print(df2.iloc[0, 1])

Progress/compute.py

@@ -23,7 +23,7 @@ df = pd.DataFrame(document, columns=['Name', 'Context'])
 # Only one Job Description should be present and in docx format
 job_docs = os.listdir(job_desc_dir)
 job_desc = tx.process(
-    job_desc_dir+job_docs[0], extension='docx', encoding='ascii')
+    job_desc_dir+job_docs[1], extension='docx', encoding='ascii')
 job_desc = str(job_desc, 'utf-8')
 job_des = Cleaner.Cleaner(job_desc[0])
 

Progress/testing_compute.ipynb

@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import textract as tx\n",
+    "import Similar\n",
+    "import Cleaner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resume_dir = \"Data/Resumes/\"\n",
+    "job_desc_dir = \"Data/JobDesc/\"\n",
+    "resume_names = os.listdir(resume_dir)\n",
+    "document = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "document "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for res in resume_names:\n",
+    "    temp = []\n",
+    "    temp.append(res)\n",
+    "    text = tx.process(resume_dir+res, encoding='ascii')\n",
+    "    text = str(text, 'utf-8')\n",
+    "    temp.append(text)\n",
+    "    document.append(temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(document, columns=['Name', 'Context'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Name</th>\n",
+       "      <th>Context</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ALANKRIT NIRJHARPremium CV Featured Resume.docx</td>\n",
+       "      <td>ALANKRIT NIRJHARPremium CV Featured Resume  \\n...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Amarpreet SinghPremium CV Featured Resume.docx</td>\n",
+       "      <td>Amarpreet SinghPremium CV Featured Resume  \\n\\...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Shambhai MishraFeatured Resume.docx</td>\n",
+       "      <td>Shambhai MishraFeatured Resume  \\n\\n \\n\\nA har...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Amruta B.pdf</td>\n",
+       "      <td>Amruta B. ThoratFeatured Resume   \\n\\n  \\n\\nJr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Dhavakumar AmarntharajanFeatured Resume.docx</td>\n",
+       "      <td>Dhavakumar AmarntharajanFeatured Resume  \\n\\n ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DIVYA PRAKASH SINGHFeatured Resume.docx</td>\n",
+       "      <td>DIVYA PRAKASH SINGHFeatured Resume  \\n\\n \\n\\nM...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Abhishek SharmaFeatured Resume.docx</td>\n",
+       "      <td>Abhishek SharmaFeatured Resume  \\n\\n \\n\\nDevel...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Manoj KumarFeatured Resume.docx</td>\n",
+       "      <td>Manoj KumarFeatured Resume  \\n\\n \\n\\nAPPLICATI...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Seshadri Sastry Kunapuli.docx</td>\n",
+       "      <td>Seshadri Sastry Kunapuli     \\n\\n \\n\\nManager ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SNEHA SAHUFeatured Resume.docx</td>\n",
+       "      <td>SNEHA SAHUFeatured Resume  \\n\\n \\n\\nMachine Le...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Bijjula SahithiFeatured Resume.docx</td>\n",
+       "      <td>Bijjula SahithiFeatured Resume  \\n\\n \\n\\nProdu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Prashant Bhat.pdf</td>\n",
+       "      <td>Prashant Bhat      \\n\\n  \\n\\nStanford Machine ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Suraj Chauhan.docx</td>\n",
+       "      <td>Suraj Chauhan   \\n\\n \\n\\nSenior Web Developer ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Nandagopal HFeatured Resume.pdf</td>\n",
+       "      <td>Nandagopal HFeatured Resume   \\n\\n  \\n\\nMYSQL,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>AJAY CHINNIFeatured Resume.docx</td>\n",
+       "      <td>AJAY CHINNIFeatured Resume  \\n\\n \\n\\nSeeking r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Ashwani Kumar RajputPremium CV Featured Resume...</td>\n",
+       "      <td>Ashwani Kumar RajputPremium CV Featured Resume...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>RISHABH SHARMAFeatured Resume.docx</td>\n",
+       "      <td>RISHABH SHARMAFeatured Resume  \\n\\n \\n\\nJunior...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>NARENDRA HINGE.docx</td>\n",
+       "      <td>NARENDRA HINGE\\n\\n\\n\\n \\n\\nSenior Web Develope...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Kaustav SenFeatured Resume.docx</td>\n",
+       "      <td>Kaustav SenFeatured Resume  \\n\\n \\n\\nTech Lead...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>khyati gandhi.docx</td>\n",
+       "      <td>khyati gandhi   \\n\\n \\n\\nWeb Developer seeking...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Jithin J NairFeatured Resume.docx</td>\n",
+       "      <td>Jithin J NairFeatured Resume  \\n\\n \\n\\nMachine...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 Name  \\\n",
+       "0     ALANKRIT NIRJHARPremium CV Featured Resume.docx   \n",
+       "1      Amarpreet SinghPremium CV Featured Resume.docx   \n",
+       "2                 Shambhai MishraFeatured Resume.docx   \n",
+       "3                                        Amruta B.pdf   \n",
+       "4        Dhavakumar AmarntharajanFeatured Resume.docx   \n",
+       "5             DIVYA PRAKASH SINGHFeatured Resume.docx   \n",
+       "6                 Abhishek SharmaFeatured Resume.docx   \n",
+       "7                     Manoj KumarFeatured Resume.docx   \n",
+       "8                       Seshadri Sastry Kunapuli.docx   \n",
+       "9                      SNEHA SAHUFeatured Resume.docx   \n",
+       "10                Bijjula SahithiFeatured Resume.docx   \n",
+       "11                                  Prashant Bhat.pdf   \n",
+       "12                                 Suraj Chauhan.docx   \n",
+       "13                    Nandagopal HFeatured Resume.pdf   \n",
+       "14                    AJAY CHINNIFeatured Resume.docx   \n",
+       "15  Ashwani Kumar RajputPremium CV Featured Resume...   \n",
+       "16                 RISHABH SHARMAFeatured Resume.docx   \n",
+       "17                                NARENDRA HINGE.docx   \n",
+       "18                    Kaustav SenFeatured Resume.docx   \n",
+       "19                                 khyati gandhi.docx   \n",
+       "20                  Jithin J NairFeatured Resume.docx   \n",
+       "\n",
+       "                                              Context  \n",
+       "0   ALANKRIT NIRJHARPremium CV Featured Resume  \\n...  \n",
+       "1   Amarpreet SinghPremium CV Featured Resume  \\n\\...  \n",
+       "2   Shambhai MishraFeatured Resume  \\n\\n \\n\\nA har...  \n",
+       "3   Amruta B. ThoratFeatured Resume   \\n\\n  \\n\\nJr...  \n",
+       "4   Dhavakumar AmarntharajanFeatured Resume  \\n\\n ...  \n",
+       "5   DIVYA PRAKASH SINGHFeatured Resume  \\n\\n \\n\\nM...  \n",
+       "6   Abhishek SharmaFeatured Resume  \\n\\n \\n\\nDevel...  \n",
+       "7   Manoj KumarFeatured Resume  \\n\\n \\n\\nAPPLICATI...  \n",
+       "8   Seshadri Sastry Kunapuli     \\n\\n \\n\\nManager ...  \n",
+       "9   SNEHA SAHUFeatured Resume  \\n\\n \\n\\nMachine Le...  \n",
+       "10  Bijjula SahithiFeatured Resume  \\n\\n \\n\\nProdu...  \n",
+       "11  Prashant Bhat      \\n\\n  \\n\\nStanford Machine ...  \n",
+       "12  Suraj Chauhan   \\n\\n \\n\\nSenior Web Developer ...  \n",
+       "13  Nandagopal HFeatured Resume   \\n\\n  \\n\\nMYSQL,...  \n",
+       "14  AJAY CHINNIFeatured Resume  \\n\\n \\n\\nSeeking r...  \n",
+       "15  Ashwani Kumar RajputPremium CV Featured Resume...  \n",
+       "16  RISHABH SHARMAFeatured Resume  \\n\\n \\n\\nJunior...  \n",
+       "17  NARENDRA HINGE\\n\\n\\n\\n \\n\\nSenior Web Develope...  \n",
+       "18  Kaustav SenFeatured Resume  \\n\\n \\n\\nTech Lead...  \n",
+       "19  khyati gandhi   \\n\\n \\n\\nWeb Developer seeking...  \n",
+       "20  Jithin J NairFeatured Resume  \\n\\n \\n\\nMachine...  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_job_description():\n",
+    "    job_docs = os.listdir(job_desc_dir)\n",
+    "    job_desc = tx.process(\n",
+    "        job_desc_dir+job_docs[1], extension='docx', encoding='ascii')\n",
+    "    job_desc = str(job_desc, 'utf-8')\n",
+    "    job_des = Cleaner.Cleaner(job_desc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = []\n",
+    "def compute_score(x=2, y=2):\n",
+    "    for text in df['Context']:\n",
+    "        raw = Cleaner.Cleaner(text)\n",
+    "        score = Similar.match(raw[2], job_des[2])\n",
+    "        scores.append(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7.188133725864406,\n",
+       " 17.082715886282017,\n",
+       " 9.476762357802984,\n",
+       " 17.291130127664918,\n",
+       " 18.012687466241466,\n",
+       " 9.452611578580793,\n",
+       " 8.42476835081202,\n",
+       " 11.586667849627627,\n",
+       " 12.456231619031753,\n",
+       " 17.0072580417408,\n",
+       " 14.108484885761968,\n",
+       " 9.982352544008108,\n",
+       " 16.236249502169258,\n",
+       " 17.05720171632391,\n",
+       " 11.320461075067941,\n",
+       " 15.613431194443613,\n",
+       " 9.533217549290569,\n",
+       " 14.925508576697014,\n",
+       " 17.989739486512008,\n",
+       " 24.064673772279484,\n",
+       " 11.435471298914607]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Scores'] = scores\n",
+    "df2 = df.sort_values(by=['Scores'], ascending=False)\n",
+    "# print(df2.iloc[0, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.text(15, 30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Progress/topic_modelling_1.ipynb

@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gensim\n",
+    "import gensim.corpora as corpora\n",
+    "from gensim.utils import simple_preprocess\n",
+    "from gensim.models import CoherenceModel\n",
+    "import re\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import Distill"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from texts import text_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = Distill.remove_stopwords(Distill.tokenize(text_2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = Distill.remove_tags(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = Distill.lemmatize(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# text = Distill._to_string(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokens_ = []\n",
+    "for a in text:\n",
+    "    tokens_.append([a])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tokens_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "id2word = corpora.Dictionary(tokens_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_token(List):\n",
+    "    # takes a simple list and breaks it into tokens of the form [[],[],[]]\n",
+    "    token = [[a] for a in List]\n",
+    "    return token"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saas = to_token(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# saas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = [id2word.doc2bow(text) for text in tokens_]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Human readable format of corpus (term-frequency)\n",
+    "# [[(id2word[id], freq) for id, freq in cp] for cp in corpus[:]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# corpus[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build LDA model\n",
+    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
+    "                                           id2word=id2word,\n",
+    "                                           num_topics=20, \n",
+    "                                           random_state=100,\n",
+    "                                           update_every=1,\n",
+    "                                           chunksize=100,\n",
+    "                                           passes=10,\n",
+    "                                           alpha='auto',\n",
+    "                                           per_word_topics=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(0,\n",
+      "  '0.087*\"property\" + 0.046*\"brake\" + 0.046*\"lead\" + 0.004*\"commonwealth\" + '\n",
+      "  '0.004*\"compensate\" + 0.004*\"design\" + 0.004*\"decade\" + 0.004*\"range\" + '\n",
+      "  '0.004*\"local\" + 0.004*\"mean\"'),\n",
+      " (1,\n",
+      "  '0.160*\"damage\" + 0.040*\"reach\" + 0.040*\"expose\" + 0.040*\"contaminate\" + '\n",
+      "  '0.004*\"range\" + 0.004*\"assessment\" + 0.004*\"design\" + 0.004*\"public\" + '\n",
+      "  '0.004*\"mean\" + 0.004*\"reimbursement\"'),\n",
+      " (2,\n",
+      "  '0.196*\"party\" + 0.070*\"private\" + 0.037*\"caustic\" + 0.037*\"action\" + '\n",
+      "  '0.004*\"mean\" + 0.004*\"design\" + 0.004*\"public\" + 0.004*\"assessment\" + '\n",
+      "  '0.004*\"commonwealth\" + 0.004*\"range\"'),\n",
+      " (3,\n",
+      "  '0.120*\"habitat\" + 0.044*\"agree\" + 0.044*\"fabric\" + 0.004*\"party\" + '\n",
+      "  '0.004*\"compensate\" + 0.004*\"design\" + 0.004*\"public\" + 0.004*\"assessment\" + '\n",
+      "  '0.004*\"commonwealth\" + 0.004*\"encourage\"'),\n",
+      " (4,\n",
+      "  '0.313*\"site\" + 0.143*\"ecological\" + 0.052*\"predecessor\" + 0.003*\"mean\" + '\n",
+      "  '0.003*\"fws\" + 0.003*\"range\" + 0.003*\"compensate\" + 0.003*\"commonwealth\" + '\n",
+      "  '0.003*\"reimbursement\" + 0.003*\"assessment\"'),\n",
+      " (5,\n",
+      "  '0.194*\"trustee\" + 0.037*\"hazardous\" + 0.037*\"well\" + 0.037*\"perform\" + '\n",
+      "  '0.037*\"doi\" + 0.004*\"settlement\" + 0.004*\"federal\" + 0.004*\"commonwealth\" + '\n",
+      "  '0.004*\"mean\" + 0.004*\"range\"'),\n",
+      " (6,\n",
+      "  '0.052*\"pay\" + 0.005*\"owner\" + 0.005*\"resource\" + 0.005*\"mean\" + '\n",
+      "  '0.005*\"design\" + 0.005*\"range\" + 0.005*\"compensate\" + 0.005*\"fws\" + '\n",
+      "  '0.005*\"commonwealth\" + 0.005*\"reimbursement\"'),\n",
+      " (7,\n",
+      "  '0.204*\"federal\" + 0.038*\"liability\" + 0.038*\"century\" + 0.038*\"current\" + '\n",
+      "  '0.004*\"range\" + 0.004*\"mean\" + 0.004*\"design\" + 0.004*\"assessment\" + '\n",
+      "  '0.004*\"commonwealth\" + 0.004*\"fws\"'),\n",
+      " (8,\n",
+      "  '0.075*\"owner\" + 0.075*\"lining\" + 0.075*\"asbestos\" + 0.039*\"former\" + '\n",
+      "  '0.039*\"clean\" + 0.004*\"compensate\" + 0.004*\"public\" + 0.004*\"assessment\" + '\n",
+      "  '0.004*\"commonwealth\" + 0.004*\"design\"'),\n",
+      " (9,\n",
+      "  '0.175*\"state\" + 0.113*\"portion\" + 0.036*\"business\" + 0.036*\"involve\" + '\n",
+      "  '0.004*\"private\" + 0.004*\"compensate\" + 0.004*\"design\" + 0.004*\"public\" + '\n",
+      "  '0.004*\"mean\" + 0.004*\"encourage\"'),\n",
+      " (10,\n",
+      "  '0.110*\"industrial\" + 0.090*\"require\" + 0.040*\"announce\" + 0.040*\"water\" + '\n",
+      "  '0.004*\"portion\" + 0.004*\"design\" + 0.004*\"compensate\" + 0.004*\"decade\" + '\n",
+      "  '0.004*\"range\" + 0.004*\"commonwealth\"'),\n",
+      " (11,\n",
+      "  '0.048*\"operation\" + 0.048*\"solution\" + 0.048*\"claim\" + 0.005*\"include\" + '\n",
+      "  '0.005*\"site\" + 0.005*\"today\" + 0.005*\"compensate\" + 0.005*\"design\" + '\n",
+      "  '0.005*\"mean\" + 0.005*\"range\"'),\n",
+      " (12,\n",
+      "  '0.146*\"wetland\" + 0.128*\"provide\" + 0.036*\"land\" + 0.036*\"facility\" + '\n",
+      "  '0.004*\"asbestos\" + 0.004*\"state\" + 0.004*\"settlement\" + 0.004*\"mean\" + '\n",
+      "  '0.004*\"design\" + 0.004*\"fws\"'),\n",
+      " (13,\n",
+      "  '0.160*\"contamination\" + 0.040*\"operate\" + 0.040*\"estimate\" + 0.040*\"result\" '\n",
+      "  '+ 0.004*\"public\" + 0.004*\"design\" + 0.004*\"fws\" + 0.004*\"commonwealth\" + '\n",
+      "  '0.004*\"compensate\" + 0.004*\"range\"'),\n",
+      " (14,\n",
+      "  '0.307*\"use\" + 0.132*\"decree\" + 0.132*\"consent\" + 0.062*\"cost\" + '\n",
+      "  '0.018*\"manufacturing\" + 0.018*\"remedial\" + 0.018*\"arsenic\" + '\n",
+      "  '0.002*\"restoration\" + 0.002*\"also\" + 0.002*\"project\"'),\n",
+      " (15,\n",
+      "  '0.168*\"include\" + 0.042*\"19th\" + 0.042*\"resolve\" + 0.004*\"mean\" + '\n",
+      "  '0.004*\"assessment\" + 0.004*\"range\" + 0.004*\"compensate\" + '\n",
+      "  '0.004*\"reimbursement\" + 0.004*\"fws\" + 0.004*\"commonwealth\"'),\n",
+      " (16,\n",
+      "  '0.584*\"settlement\" + 0.057*\"today\" + 0.018*\"manufacture\" + 0.018*\"acre\" + '\n",
+      "  '0.002*\"restoration\" + 0.002*\"public\" + 0.002*\"MassDEPs\" + 0.002*\"work\" + '\n",
+      "  '0.002*\"comment\" + 0.002*\"fund\"'),\n",
+      " (17,\n",
+      "  '0.250*\"groundwater\" + 0.036*\"substance\" + 0.036*\"cotton\" + 0.036*\"various\" '\n",
+      "  '+ 0.004*\"natural\" + 0.004*\"mean\" + 0.004*\"range\" + 0.004*\"compensate\" + '\n",
+      "  '0.004*\"assessment\" + 0.004*\"commonwealth\"'),\n",
+      " (18,\n",
+      "  '0.273*\"natural\" + 0.067*\"injury\" + 0.067*\"lodge\" + 0.030*\"clutch\" + '\n",
+      "  '0.030*\"back\" + 0.003*\"range\" + 0.003*\"commonwealth\" + 0.003*\"design\" + '\n",
+      "  '0.003*\"assessment\" + 0.003*\"mean\"'),\n",
+      " (19,\n",
+      "  '0.315*\"resource\" + 0.031*\"large\" + 0.031*\"operator\" + 0.031*\"date\" + '\n",
+      "  '0.031*\"1million\" + 0.003*\"property\" + 0.003*\"compensate\" + 0.003*\"design\" + '\n",
+      "  '0.003*\"public\" + 0.003*\"organization\"')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the Keyword in the 10 topics\n",
+    "pprint(lda_model.print_topics())\n",
+    "doc_lda = lda_model[corpus]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Perplexity:  -20.580518818099517\n",
+      "\n",
+      "Coherence Score:  0.8399551092841173\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute Perplexity\n",
+    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
+    "\n",
+    "# Compute Coherence Score\n",
+    "coherence_model_lda = CoherenceModel(model=lda_model, texts=tokens_, dictionary=id2word, coherence='c_v')\n",
+    "coherence_lda = coherence_model_lda.get_coherence()\n",
+    "print('\\nCoherence Score: ', coherence_lda)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plotting tools\n",
+    "import pyLDAvis\n",
+    "import pyLDAvis.gensim  # don't skip this\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Visualize the topics\n",
+    "# pyLDAvis.enable_notebook()\n",
+    "# vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
+    "# vis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Progress/topic_modelling_resumes.ipynb

@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gensim\n",
+    "import gensim.corpora as corpora\n",
+    "from gensim.utils import simple_preprocess\n",
+    "from gensim.models import CoherenceModel\n",
+    "import re\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from pprint import pprint\n",
+    "import Cleaner\n",
+    "import Similar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import textract as tx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resume_dir = \"Data/Resumes/\"\n",
+    "job_desc_dir = \"Data/JobDesc/\"\n",
+    "resume_names = os.listdir(resume_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document = []\n",
+    "for res in resume_names:\n",
+    "    text = tx.process(resume_dir+res, encoding='ascii')\n",
+    "    text = str(text, 'utf-8')\n",
+    "    temp = Cleaner.Cleaner(text)\n",
+    "    document.append(temp[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "id2word = corpora.Dictionary(document)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = [id2word.doc2bow(text) for text in document]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
+    "                                           id2word=id2word,\n",
+    "                                           num_topics=2, \n",
+    "                                           random_state=100,\n",
+    "                                           update_every=1,\n",
+    "                                           chunksize=100,\n",
+    "                                           passes=50,\n",
+    "                                           alpha='auto',\n",
+    "                                           per_word_topics=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(0,\n",
+      "  '0.002*\"role\" + 0.002*\"Experience\" + 0.002*\"Services\" + 0.002*\"Skills\" + '\n",
+      "  '0.002*\"Skill\" + 0.002*\"Date\" + 0.002*\"skill\" + 0.002*\"back\" + 0.002*\"Area\" '\n",
+      "  '+ 0.002*\"last\"'),\n",
+      " (1,\n",
+      "  '0.003*\"IT\" + 0.003*\"Pref\" + 0.003*\"ID\" + 0.003*\"Active\" + 0.003*\"UG\" + '\n",
+      "  '0.003*\"d\" + 0.003*\"Current\" + 0.003*\"experience\" + 0.003*\"Summary\" + '\n",
+      "  '0.003*\"Months\"')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(lda_model.print_topics())\n",
+    "doc_lda = lda_model[corpus]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyLDAvis\n",
+    "import pyLDAvis.gensim  # don't skip this\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el22281400102997868488005527649\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el22281400102997868488005527649_data = {\"mdsDat\": {\"x\": [0.045266102999448776, -0.045266102999448776], \"y\": [0.0, 0.0], \"topics\": [1, 2], \"cluster\": [1, 1], \"Freq\": [50.73581314086914, 49.264190673828125]}, \"tinfo\": {\"Term\": [\"improve\", \"less\", \"Days\", \"program\", \"reduce\", \"Technologies\", \"Sep\", \"lead\", \"NCR\", \"SQL\", \"notice\", \"Leader\", \"detect\", \"duration\", \"extensive\", \"track\", \"prediction\", \"structure\", \"document\", \"MTech\", \"enhance\", \"apply\", \"Apr\", \"user\", \"May\", \"Jump\", \"service\", \"cycle\", \"troubleshoot\", \"full\", \"NCR\", \"service\", \"cycle\", \"troubleshoot\", \"full\", \"cloud\", \"text\", \"actively\", \"leadership\", \"MS\", \"MCA\", \"Days\", \"less\", \"look\", \"sequence\", \"GCP\", \"monitoring\", \"junior\", \"level\", \"June\", \"transfer\", \"LearningDeep\", \"JDK\", \"box\", \"JIRA\", \"fashion\", \"pretraine\", \"domain\", \"working\", \"accordingly\", \"Marathi\", \"Jquery\", \"SQL\", \"client\", \"perform\", \"Marital\", \"notice\", \"Jump\", \"singleunmarrie\", \"degree\", \"professional\", \"SectionWork\", \"Resume\", \"IT\", \"Modified\", \"industry\", \"Highest\", \"Pref\", \"d\", \"UG\", \"ID\", \"Active\", \"experience\", \"Current\", \"Summary\", \"Months\", \"Location\", \"Status\", \"Phone\", \"Period\", \"Education\", \"total\", \"Functional\", \"year\", \"Key\", \"Number\", \"Email\", \"it\", \"Top\", \"version\", \"use\", \"back\", \"Name\", \"skill\", \"Area\", \"last\", \"Date\", \"Skills\", \"improve\", \"reduce\", \"program\", \"Technologies\", \"Leader\", \"track\", \"prediction\", \"extensive\", \"duration\", \"detect\", \"structure\", \"document\", \"enhance\", \"MTech\", \"apply\", \"value\", \"many\", \"control\", \"artificial\", \"Till\", \"tuning\", \"coordinate\", \"Numpy\", \"index\", \"enable\", \"Entity\", \"suggest\", \"steering\", \"architecture\", \"OpenCV\", \"Gradient\", \"camera\", \"Sep\", \"lead\", \"information\", \"identification\", \"Apr\", \"user\", \"May\", \"code\", \"identify\", \"performance\", \"algorithm\", \"deliver\", \"website\", \"section\", \"component\", \"css\", \"Maintenance\", \"company\", \"Nov\", \"issue\", \"test\", \"new\", \"system\", \"design\", \"develop\", \"role\", \"datum\", \"Computer\", \"Experience\", \"Services\", \"Engineering\", \"application\", \"ITSoftwareSoftware\", \"marital\", \"Skills\", \"Skill\", \"Date\", \"Area\", \"back\", \"skill\", \"last\", \"Name\", \"it\", \"version\", \"use\", \"Top\", \"Number\", \"Email\", \"year\", \"Key\", \"Education\", \"Period\", \"total\", \"Functional\", \"Status\", \"Phone\", \"Months\", \"Location\", \"ID\", \"UG\", \"d\", \"experience\", \"Summary\", \"Active\", \"Pref\"], \"Freq\": [4.0, 6.0, 6.0, 3.0, 3.0, 3.0, 5.0, 5.0, 3.0, 7.0, 10.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 12.0, 2.0, 2.0, 2.0, 2.0, 3.0877420902252197, 2.4015042781829834, 2.4015040397644043, 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\"Modified\", \"Months\", \"Months\", \"NCR\", \"Name\", \"Name\", \"Nov\", \"Nov\", \"Number\", \"Number\", \"Numpy\", \"OpenCV\", \"Period\", \"Period\", \"Phone\", \"Phone\", \"Pref\", \"Pref\", \"Resume\", \"Resume\", \"SQL\", \"SQL\", \"SectionWork\", \"SectionWork\", \"Sep\", \"Sep\", \"Services\", \"Services\", \"Skill\", \"Skill\", \"Skills\", \"Skills\", \"Status\", \"Status\", \"Summary\", \"Summary\", \"Technologies\", \"Till\", \"Top\", \"Top\", \"UG\", \"UG\", \"accordingly\", \"actively\", \"algorithm\", \"algorithm\", \"application\", \"application\", \"apply\", \"architecture\", \"artificial\", \"back\", \"back\", \"box\", \"camera\", \"client\", \"client\", \"cloud\", \"code\", \"code\", \"company\", \"company\", \"component\", \"component\", \"control\", \"coordinate\", \"css\", \"css\", \"cycle\", \"d\", \"d\", \"datum\", \"datum\", \"degree\", \"degree\", \"deliver\", \"deliver\", \"design\", \"design\", \"detect\", \"develop\", \"develop\", \"document\", \"domain\", \"duration\", \"enable\", \"enhance\", \"experience\", \"experience\", \"extensive\", \"fashion\", \"full\", \"identification\", \"identify\", \"identify\", \"improve\", \"index\", \"industry\", \"industry\", \"information\", \"issue\", \"issue\", \"it\", \"it\", \"junior\", \"last\", \"last\", \"lead\", \"lead\", \"leadership\", \"less\", \"less\", \"level\", \"look\", \"many\", \"marital\", \"marital\", \"monitoring\", \"new\", \"new\", \"notice\", \"notice\", \"perform\", \"perform\", \"performance\", \"performance\", \"prediction\", \"pretraine\", \"professional\", \"professional\", \"program\", \"reduce\", \"role\", \"role\", \"section\", \"section\", \"sequence\", \"service\", \"singleunmarrie\", \"singleunmarrie\", \"skill\", \"skill\", \"steering\", \"structure\", \"suggest\", \"system\", \"system\", \"test\", \"test\", \"text\", \"total\", \"total\", \"track\", \"transfer\", \"troubleshoot\", \"tuning\", \"use\", \"use\", \"user\", \"user\", \"value\", \"version\", \"version\", \"website\", \"website\", \"working\", \"year\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 1]};\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_el22281400102997868488005527649\", ldavis_el22281400102997868488005527649_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://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el22281400102997868488005527649\", ldavis_el22281400102997868488005527649_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el22281400102997868488005527649\", ldavis_el22281400102997868488005527649_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "PreparedData(topic_coordinates=              x    y  topics  cluster       Freq\n",
+       "topic                                           \n",
+       "1      0.045266  0.0       1        1  50.735813\n",
+       "0     -0.045266  0.0       2        1  49.264191, topic_info=            Term      Freq      Total Category  logprob  loglift\n",
+       "759      improve  4.000000   4.000000  Default  30.0000  30.0000\n",
+       "565         less  6.000000   6.000000  Default  29.0000  29.0000\n",
+       "520         Days  6.000000   6.000000  Default  28.0000  28.0000\n",
+       "781      program  3.000000   3.000000  Default  27.0000  27.0000\n",
+       "1734      reduce  3.000000   3.000000  Default  26.0000  26.0000\n",
+       "...          ...       ...        ...      ...      ...      ...\n",
+       "185            d  5.775256  15.039238   Topic2  -6.1555  -0.2491\n",
+       "197   experience  5.775257  15.039238   Topic2  -6.1555  -0.2491\n",
+       "152      Summary  5.775258  15.039238   Topic2  -6.1555  -0.2491\n",
+       "0         Active  5.775256  15.039238   Topic2  -6.1555  -0.2491\n",
+       "117         Pref  5.775254  15.039238   Topic2  -6.1555  -0.2491\n",
+       "\n",
+       "[205 rows x 6 columns], token_table=      Topic      Freq     Term\n",
+       "term                          \n",
+       "0         1  0.598435   Active\n",
+       "0         2  0.398956   Active\n",
+       "1107      1  0.209824      Apr\n",
+       "1107      2  0.839295      Apr\n",
+       "7         1  0.627047     Area\n",
+       "...     ...       ...      ...\n",
+       "671       1  0.244713  website\n",
+       "671       2  0.734140  website\n",
+       "515       1  0.973230  working\n",
+       "271       1  0.598435     year\n",
+       "271       2  0.398956     year\n",
+       "\n",
+       "[226 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Visualize the topics\n",
+    "pyLDAvis.enable_notebook()\n",
+    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
+    "vis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}