3. TF-IDF and LSA-LDA.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) Download 5 JSON files from Indiegogo dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data\n",
    "df1 = pd.read_csv('indiegogo_2023/Indiegogo1.csv')\n",
    "df2 = pd.read_csv('indiegogo_2023/Indiegogo2.csv')\n",
    "df3 = pd.read_csv('indiegogo_2023/Indiegogo3.csv')\n",
    "df4 = pd.read_csv('indiegogo_2023/Indiegogo4.csv')\n",
    "df5 = pd.read_csv('indiegogo_2023/Indiegogo5.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the data into a list\n",
    "list1 = df1['title'].to_list()\n",
    "list2 = df2['title'].to_list()\n",
    "list3 = df3['title'].to_list()\n",
    "list4 = df4['title'].to_list()\n",
    "list5 = df5['title'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing value in the list:  0\n",
      "Missing value in the list:  34\n",
      "Duplicate value in the list:  111861\n"
     ]
    }
   ],
   "source": [
    "# Combine the lists\n",
    "title_list = list1 + list2 + list3 + list4 + list5\n",
    "# Check if there's any missing value\n",
    "print('Missing value in the list: ', title_list.count(''))\n",
    "print('Missing value in the list: ', title_list.count(np.nan))\n",
    "# Check if there's any duplicate value\n",
    "print('Duplicate value in the list: ', len(title_list) - len(set(title_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing value in the list:  0\n",
      "Length of the list:  24956\n"
     ]
    }
   ],
   "source": [
    "# Remove the np.nan value\n",
    "title_list = [x for x in title_list if str(x) != 'nan']\n",
    "print('Missing value in the list: ', title_list.count(np.nan))\n",
    "# Remove the duplicate value\n",
    "title_list = list(set(title_list))\n",
    "# Check the length of the list\n",
    "print('Length of the list: ', len(title_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use 3 Texual Feature Engineering Methods reducing sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.parsing.preprocessing import remove_stopwords\n",
    "from gensim.utils import simple_preprocess\n",
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 3 textual feature engineering methods\n",
    "# 1. Remove the stop words\n",
    "removed_title = [remove_stopwords(title) for title in title_list]\n",
    "# 2. Tokenize the text\n",
    "tokenized_title = [simple_preprocess(rtitle, deacc=True) for rtitle in removed_title] # deacc=True removes punctuations\n",
    "# 3. Stemming\n",
    "ps = PorterStemmer()\n",
    "stemmed_title = [[ps.stem(word) for word in token] for token in tokenized_title]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24906"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the empty list from stemmed_title\n",
    "stemmed_title = [x for x in stemmed_title if x != []]\n",
    "len(stemmed_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>make jamaica clean jamaica green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trelawear discreet emerg respons necklac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jello shot anim thesi film</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>greek bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>help child refuge now</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      title\n",
       "0          make jamaica clean jamaica green\n",
       "1  trelawear discreet emerg respons necklac\n",
       "2                jello shot anim thesi film\n",
       "3                                 greek bee\n",
       "4                     help child refuge now"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_list = [' '.join(clean_title) for clean_title in stemmed_title]\n",
    "# transform stemmed_title into a dataframe\n",
    "clean_df = pd.DataFrame(clean_list)\n",
    "clean_df.columns = ['title']\n",
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) Generate term-document matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Doc 1  Doc 2  Doc 3  Doc 4  Doc 5  Doc 6  Doc 7  Doc 8  Doc 9  \\\n",
      "the          0      0      0      0      0      0      1      0      0   \n",
      "help         0      0      0      0      1      0      0      0      0   \n",
      "world        0      0      0      0      0      0      0      0      0   \n",
      "film         0      0      1      0      0      0      0      0      0   \n",
      "new          0      0      0      0      0      0      0      0      0   \n",
      "game         0      0      0      0      0      0      0      0      0   \n",
      "book         0      0      0      0      0      0      0      0      0   \n",
      "project      0      0      0      0      0      0      0      0      1   \n",
      "your         0      0      0      0      0      0      0      0      0   \n",
      "short        0      0      0      0      0      0      0      0      0   \n",
      "for          0      0      0      0      0      0      0      0      0   \n",
      "fund         0      0      0      0      0      0      0      0      0   \n",
      "first        0      0      0      0      0      0      0      0      0   \n",
      "in           0      0      0      0      0      0      0      0      0   \n",
      "seri         0      0      0      0      0      0      0      0      0   \n",
      "art          0      0      0      0      0      0      0      0      0   \n",
      "smart        0      0      0      0      0      0      0      0      0   \n",
      "power        0      0      0      0      0      0      0      0      0   \n",
      "camera       0      0      0      0      0      0      0      0      0   \n",
      "save         0      0      0      0      0      0      0      0      0   \n",
      "app          0      0      0      0      0      0      0      0      0   \n",
      "music        0      0      0      0      0      0      0      0      0   \n",
      "album        0      0      0      0      0      0      0      0      0   \n",
      "bike         0      0      0      0      0      0      0      0      0   \n",
      "one          0      0      0      0      0      0      0      0      0   \n",
      "\n",
      "         Doc 10  ...  Doc 24897  Doc 24898  Doc 24899  Doc 24900  Doc 24901  \\\n",
      "the           0  ...          1          0          0          0          0   \n",
      "help          0  ...          0          0          1          0          0   \n",
      "world         0  ...          0          0          0          0          0   \n",
      "film          0  ...          0          0          0          0          0   \n",
      "new           0  ...          0          0          0          0          0   \n",
      "game          0  ...          0          0          0          0          0   \n",
      "book          0  ...          0          0          0          0          0   \n",
      "project       0  ...          0          0          0          0          0   \n",
      "your          0  ...          0          0          0          0          0   \n",
      "short         0  ...          0          0          0          0          0   \n",
      "for           0  ...          0          0          0          0          0   \n",
      "fund          0  ...          0          0          0          0          0   \n",
      "first         0  ...          0          0          0          0          0   \n",
      "in            0  ...          0          0          0          0          0   \n",
      "seri          0  ...          0          0          1          0          0   \n",
      "art           0  ...          0          0          0          0          0   \n",
      "smart         0  ...          0          0          0          0          0   \n",
      "power         0  ...          0          0          0          0          0   \n",
      "camera        0  ...          0          0          0          0          0   \n",
      "save          0  ...          0          0          0          0          0   \n",
      "app           0  ...          0          0          0          0          0   \n",
      "music         0  ...          0          0          0          0          0   \n",
      "album         0  ...          0          0          0          0          0   \n",
      "bike          0  ...          0          0          0          0          0   \n",
      "one           0  ...          0          0          0          0          0   \n",
      "\n",
      "         Doc 24902  Doc 24903  Doc 24904  Doc 24905  Doc 24906  \n",
      "the              0          0          0          1          0  \n",
      "help             0          0          0          0          1  \n",
      "world            0          0          0          0          0  \n",
      "film             0          0          0          0          0  \n",
      "new              0          0          0          0          0  \n",
      "game             0          0          0          0          0  \n",
      "book             0          0          0          0          0  \n",
      "project          0          0          0          0          0  \n",
      "your             0          0          0          0          0  \n",
      "short            0          0          0          0          0  \n",
      "for              0          0          0          0          0  \n",
      "fund             1          0          0          0          0  \n",
      "first            0          0          0          0          0  \n",
      "in               0          0          0          0          0  \n",
      "seri             0          0          0          0          0  \n",
      "art              0          0          0          0          0  \n",
      "smart            0          0          0          0          0  \n",
      "power            0          0          0          0          0  \n",
      "camera           0          0          0          0          0  \n",
      "save             0          0          0          0          0  \n",
      "app              0          0          0          0          0  \n",
      "music            0          0          0          0          0  \n",
      "album            0          0          0          0          0  \n",
      "bike             0          0          0          0          0  \n",
      "one              0          0          0          0          0  \n",
      "\n",
      "[25 rows x 24906 columns]\n"
     ]
    }
   ],
   "source": [
    "# Activate CountVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Count Vectorizer\n",
    "vect = CountVectorizer()  \n",
    "vects = vect.fit_transform(clean_df['title'])\n",
    "\n",
    "# Select from the data set\n",
    "td = pd.DataFrame(vects.todense())  \n",
    "td.columns = vect.get_feature_names_out()\n",
    "tdmatrix = td.T\n",
    "tdmatrix.columns = ['Doc '+str(i) for i in range(1, len(tdmatrix.columns)+1)]\n",
    "tdmatrix['total_count'] = tdmatrix.sum(axis=1)\n",
    "\n",
    "# Select top 25 words\n",
    "tdmatrix_25 = tdmatrix.sort_values(by ='total_count',ascending=False)[:25] \n",
    "# Print the 25 rows \n",
    "print(tdmatrix_25.drop(columns=['total_count']))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) Apply TF/IDF on the matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        aa  aaa  aadongl  aaib  aamjiwnaang  aapi  aarogyaseva  aaron  aarzu  \\\n",
      "0      1.0  0.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "1      0.0  1.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "2      0.0  0.0      1.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "3      0.0  0.0      0.0   1.0          0.0   0.0          0.0    0.0    0.0   \n",
      "4      0.0  0.0      0.0   0.0          1.0   0.0          0.0    0.0    0.0   \n",
      "...    ...  ...      ...   ...          ...   ...          ...    ...    ...   \n",
      "21728  0.0  0.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "21729  0.0  0.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "21730  0.0  0.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "21731  0.0  0.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "21732  0.0  0.0      0.0   0.0          0.0   0.0          0.0    0.0    0.0   \n",
      "\n",
      "       aasana  ...  丨the  九月初九   咒文   坊茶  小豆釘廣東話親子互動劇場  整合型充電站  蝶のために壊れた顎  \\\n",
      "0         0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "1         0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "2         0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "3         0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "4         0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "...       ...  ...   ...   ...  ...  ...           ...     ...        ...   \n",
      "21728     0.0  ...   0.0   0.0  0.0  0.0           0.0     1.0        0.0   \n",
      "21729     0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        1.0   \n",
      "21730     0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "21731     0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "21732     0.0  ...   0.0   0.0  0.0  0.0           0.0     0.0        0.0   \n",
      "\n",
      "       请帮助支持我的大学教育  遅咲き  霓虹招牌手稿出版計劃  \n",
      "0              0.0  0.0         0.0  \n",
      "1              0.0  0.0         0.0  \n",
      "2              0.0  0.0         0.0  \n",
      "3              0.0  0.0         0.0  \n",
      "4              0.0  0.0         0.0  \n",
      "...            ...  ...         ...  \n",
      "21728          0.0  0.0         0.0  \n",
      "21729          0.0  0.0         0.0  \n",
      "21730          1.0  0.0         0.0  \n",
      "21731          0.0  1.0         0.0  \n",
      "21732          0.0  0.0         1.0  \n",
      "\n",
      "[21733 rows x 21733 columns]\n"
     ]
    }
   ],
   "source": [
    "tfidf = TfidfVectorizer()\n",
    "train_data = tfidf.fit_transform(tdmatrix.T)\n",
    "tfidf_df = pd.DataFrame(train_data.toarray(),columns=tfidf.get_feature_names_out())\n",
    "print(tfidf_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(5) Use LSA and LDA to cluster the result of the term-document matrix and visualize the clustering result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim import corpora, models, matutils\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 19\u001b[0m\n\u001b[1;32m     17\u001b[0m lsa_gensim \u001b[39m=\u001b[39m models\u001b[39m.\u001b[39mLsiModel(matutils\u001b[39m.\u001b[39mSparse2Corpus(train_data\u001b[39m.\u001b[39mT), id2word\u001b[39m=\u001b[39mdictionary, num_topics\u001b[39m=\u001b[39mnum_topics)\n\u001b[1;32m     18\u001b[0m coherencemodel \u001b[39m=\u001b[39m CoherenceModel(model\u001b[39m=\u001b[39mlsa_gensim, texts\u001b[39m=\u001b[39mstemmed_title, dictionary\u001b[39m=\u001b[39mdictionary, coherence\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mc_v\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 19\u001b[0m coherence_values\u001b[39m.\u001b[39mappend(coherencemodel\u001b[39m.\u001b[39;49mget_coherence())\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/gensim/models/coherencemodel.py:614\u001b[0m, in \u001b[0;36mCoherenceModel.get_coherence\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    605\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_coherence\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    606\u001b[0m     \u001b[39m\"\"\"Get coherence value based on pipeline parameters.\u001b[39;00m\n\u001b[1;32m    607\u001b[0m \n\u001b[1;32m    608\u001b[0m \u001b[39m    Returns\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    612\u001b[0m \n\u001b[1;32m    613\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 614\u001b[0m     confirmed_measures \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_coherence_per_topic()\n\u001b[1;32m    615\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maggregate_measures(confirmed_measures)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/gensim/models/coherencemodel.py:574\u001b[0m, in \u001b[0;36mCoherenceModel.get_coherence_per_topic\u001b[0;34m(self, segmented_topics, with_std, with_support)\u001b[0m\n\u001b[1;32m    572\u001b[0m     segmented_topics \u001b[39m=\u001b[39m measure\u001b[39m.\u001b[39mseg(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtopics)\n\u001b[1;32m    573\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accumulator \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 574\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mestimate_probabilities(segmented_topics)\n\u001b[1;32m    576\u001b[0m kwargs \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(with_std\u001b[39m=\u001b[39mwith_std, with_support\u001b[39m=\u001b[39mwith_support)\n\u001b[1;32m    577\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcoherence \u001b[39min\u001b[39;00m BOOLEAN_DOCUMENT_BASED \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcoherence \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mc_w2v\u001b[39m\u001b[39m'\u001b[39m:\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/gensim/models/coherencemodel.py:546\u001b[0m, in \u001b[0;36mCoherenceModel.estimate_probabilities\u001b[0;34m(self, segmented_topics)\u001b[0m\n\u001b[1;32m    543\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcoherence \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mc_w2v\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m    544\u001b[0m         kwargs[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkeyed_vectors\n\u001b[0;32m--> 546\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accumulator \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmeasure\u001b[39m.\u001b[39;49mprob(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    548\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accumulator\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/gensim/topic_coherence/probability_estimation.py:156\u001b[0m, in \u001b[0;36mp_boolean_sliding_window\u001b[0;34m(texts, segmented_topics, dictionary, window_size, processes)\u001b[0m\n\u001b[1;32m    154\u001b[0m     accumulator \u001b[39m=\u001b[39m ParallelWordOccurrenceAccumulator(processes, top_ids, dictionary)\n\u001b[1;32m    155\u001b[0m logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39musing \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m to estimate probabilities from sliding windows\u001b[39m\u001b[39m\"\u001b[39m, accumulator)\n\u001b[0;32m--> 156\u001b[0m \u001b[39mreturn\u001b[39;00m accumulator\u001b[39m.\u001b[39;49maccumulate(texts, window_size)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/gensim/topic_coherence/text_analysis.py:437\u001b[0m, in \u001b[0;36mParallelWordOccurrenceAccumulator.accumulate\u001b[0;34m(self, texts, window_size)\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39maccumulate\u001b[39m(\u001b[39mself\u001b[39m, texts, window_size):\n\u001b[0;32m--> 437\u001b[0m     workers, input_q, output_q \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstart_workers(window_size)\n\u001b[1;32m    438\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    439\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mqueue_all_texts(input_q, texts, window_size)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/gensim/topic_coherence/text_analysis.py:471\u001b[0m, in \u001b[0;36mParallelWordOccurrenceAccumulator.start_workers\u001b[0;34m(self, window_size)\u001b[0m\n\u001b[1;32m    469\u001b[0m     accumulator \u001b[39m=\u001b[39m PatchedWordOccurrenceAccumulator(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelevant_ids, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdictionary)\n\u001b[1;32m    470\u001b[0m     worker \u001b[39m=\u001b[39m AccumulatingWorker(input_q, output_q, accumulator, window_size)\n\u001b[0;32m--> 471\u001b[0m     worker\u001b[39m.\u001b[39;49mstart()\n\u001b[1;32m    472\u001b[0m     workers\u001b[39m.\u001b[39mappend(worker)\n\u001b[1;32m    474\u001b[0m \u001b[39mreturn\u001b[39;00m workers, input_q, output_q\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/multiprocessing/process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mnot\u001b[39;00m _current_process\u001b[39m.\u001b[39m_config\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mdaemon\u001b[39m\u001b[39m'\u001b[39m), \\\n\u001b[1;32m    119\u001b[0m        \u001b[39m'\u001b[39m\u001b[39mdaemonic processes are not allowed to have children\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    120\u001b[0m _cleanup()\n\u001b[0;32m--> 121\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_Popen(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sentinel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen\u001b[39m.\u001b[39msentinel\n\u001b[1;32m    123\u001b[0m \u001b[39m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[1;32m    124\u001b[0m \u001b[39m# reference to the process object (see bpo-30775)\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/multiprocessing/context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[1;32m    223\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[0;32m--> 224\u001b[0m     \u001b[39mreturn\u001b[39;00m _default_context\u001b[39m.\u001b[39;49mget_context()\u001b[39m.\u001b[39;49mProcess\u001b[39m.\u001b[39;49m_Popen(process_obj)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/multiprocessing/context.py:284\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[1;32m    282\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[1;32m    283\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mpopen_spawn_posix\u001b[39;00m \u001b[39mimport\u001b[39;00m Popen\n\u001b[0;32m--> 284\u001b[0m     \u001b[39mreturn\u001b[39;00m Popen(process_obj)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/multiprocessing/popen_spawn_posix.py:32\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, process_obj):\n\u001b[1;32m     31\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fds \u001b[39m=\u001b[39m []\n\u001b[0;32m---> 32\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(process_obj)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/multiprocessing/popen_fork.py:19\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturncode \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalizer \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_launch(process_obj)\n",
      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/multiprocessing/popen_spawn_posix.py:62\u001b[0m, in \u001b[0;36mPopen._launch\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msentinel \u001b[39m=\u001b[39m parent_r\n\u001b[1;32m     61\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(parent_w, \u001b[39m'\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m'\u001b[39m, closefd\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m---> 62\u001b[0m         f\u001b[39m.\u001b[39;49mwrite(fp\u001b[39m.\u001b[39;49mgetbuffer())\n\u001b[1;32m     63\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m     64\u001b[0m     fds_to_close \u001b[39m=\u001b[39m []\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Compute c_v coherence for various number of topics to determine the best number of topics\n",
    "coherence_values = []\n",
    "model_list = []\n",
    "dictionary = corpora.Dictionary(stemmed_title)\n",
    "for num_topics in range(1, 20):\n",
    "    # generate LSA model\n",
    "    # Vectorize document using TF-IDF\n",
    "    tfidf = TfidfVectorizer(lowercase=True, stop_words='english', ngram_range = (1,1))\n",
    "    # Fit and Transform the documents\n",
    "    train_data = tfidf.fit_transform(tdmatrix.T) \n",
    "    # Create SVD object\n",
    "    lsa = TruncatedSVD(n_components=num_topics, n_iter=100, random_state=42)\n",
    "    # Fit SVD model on data\n",
    "    lsa.fit_transform(train_data)\n",
    "    model_list.append(lsa)\n",
    "    # Convert the sklearn LSA model to a Gensim LsiModel for coherence calculation\n",
    "    lsa_gensim = models.LsiModel(matutils.Sparse2Corpus(train_data.T), id2word=dictionary, num_topics=num_topics)\n",
    "    coherencemodel = CoherenceModel(model=lsa_gensim, texts=stemmed_title, dictionary=dictionary, coherence='c_v')\n",
    "    coherence_values.append(coherencemodel.get_coherence())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the coherence values\n",
    "import matplotlib.pyplot as plt\n",
    "x = range(1, 20)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Number of Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.title('LSA Model Coherence score Vs number of topics')\n",
    "plt.show()\n",
    "# 1,3,10 are the good number of topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  ['sbin', 'salvag', 'rac', 'latticia', 'ganhub']\n",
      "Topic 1:  ['main', 'bonni', 'insol', 'potter', 'peppichino']\n",
      "Topic 2:  ['indigiblock', 'alterna', 'contro', 'kiskeya', 'detect']\n",
      "Topic 3:  ['pureterg', 'alain', 'pocketdrum', 'ranchland', 'demolit']\n",
      "Topic 4:  ['akhenaten', 'servic', 'coolit', 'cultivar', 'bacon']\n",
      "Topic 5:  ['gnome', 'irrit', 'microraptor', 'opedspac', 'fastpitch']\n",
      "Topic 6:  ['udgivels', 'duffel', 'cinci', 'rightplantz', 'tonic']\n",
      "Topic 7:  ['skid', 'isra', 'ro', 'aout', 'finland']\n",
      "Topic 8:  ['knoxvil', 'orthot', 'screeneo', 'underwood', 'parkinson']\n",
      "Topic 9:  ['ambulatori', 'kma', 'rockpit', 'digo', 'myrgh']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use LSA (Latent Semantic Analysis) to cluster the result of the Term-Document matrix \n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Vectorize document using TF-IDF\n",
    "tfidf = TfidfVectorizer(lowercase=True, stop_words='english', ngram_range = (1,1))\n",
    "# Fit and Transform the documents\n",
    "train_data = tfidf.fit_transform(tdmatrix.T) \n",
    "# Define the number of topics or components\n",
    "num_components = 10\n",
    "# Create SVD object\n",
    "lsa = TruncatedSVD(n_components=num_components, n_iter=100, random_state=42)\n",
    "# Fit SVD model on data\n",
    "lsa.fit_transform(train_data)\n",
    "# Get Singular values and Components\n",
    "Sigma = lsa.singular_values_\n",
    "V_transpose = lsa.components_.T\n",
    "# Print the topics with their terms\n",
    "terms = tfidf.get_feature_names_out()\n",
    "for index, component in enumerate(lsa.components_):\n",
    " zipped = zip(terms, component)\n",
    " top_terms_key=sorted(zipped, key = lambda t: t[1], reverse=True)[:5]\n",
    " top_terms_list=list(dict(top_terms_key).keys())\n",
    " print(\"Topic \"+str(index)+\": \",top_terms_list)\n",
    "\n",
    "import seaborn as sns\n",
    "compnt = pd.DataFrame(lsa.components_, columns=terms)\n",
    "# Plot the heatmap and set colorbar\n",
    "plt.figure(figsize=(8, 8))\n",
    "sns.heatmap(compnt.head(10), vmin=-0.01, vmax=0.01, center=0, yticklabels=1)\n",
    "plt.xlabel('Terms')\n",
    "plt.ylabel('Topics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LDAModel\n",
    "import gensim\n",
    "# To generate an LDA model using Gensim, we need to convert the term-document matrix from the scikit-learn format \n",
    "# to a Gensim compatible format (BoW representation). \n",
    "# Create a dictionary from the stemmed titles\n",
    "# Create a term-document matrix using the dictionary\n",
    "corpus = [dictionary.doc2bow(doc) for doc in stemmed_title]\n",
    "# Compute c_v coherence for various number of topics to determine the best number of topics\n",
    "coherence_values = []\n",
    "model_list = []\n",
    "for num_topics in range(1, 20):\n",
    "    # generate LDA model\n",
    "    ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=num_topics, id2word = dictionary, passes=20)\n",
    "    doc_lda = ldamodel[corpus]\n",
    "    coherencemodel = CoherenceModel(model=ldamodel, texts=stemmed_title, dictionary=dictionary, coherence='c_v')\n",
    "    coherence_values.append(coherencemodel.get_coherence())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the coherence values\n",
    "import matplotlib.pyplot as plt\n",
    "x = range(1, 20)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Number of Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.title('LDA Model Coherence score Vs number of topics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "5d4f326f70756f0df5980a9bff8030fd9e583c638a6e6a0773fd65146e5d3d63"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}