6.6 LSA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.6 LSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from gensim import corpora\n",
    "from gensim.models import LsiModel\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"news_articles.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "    }\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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25626</td>\n",
       "      <td>One Weight-Loss Approach Fits All? No, Not Eve...</td>\n",
       "      <td>Dr. Frank Sacks, a professor of nutrition at H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19551</td>\n",
       "      <td>South Carolina Stuns Baylor to Reach the Round...</td>\n",
       "      <td>South Carolina’s win over   Duke was not only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25221</td>\n",
       "      <td>U.S. Presidential Race, Apple, Gene Wilder: Yo...</td>\n",
       "      <td>(Want to get this briefing by email? Here’s th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18026</td>\n",
       "      <td>His Predecessor Gone, Gambia’s New President F...</td>\n",
       "      <td>BANJUL, Gambia  —   A week after he was inaugu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21063</td>\n",
       "      <td>‘Harry Potter and the Cursed Child’ Goes From ...</td>\n",
       "      <td>The biggest book of the summer isn’t a blockbu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              title  \\\n",
       "0  25626  One Weight-Loss Approach Fits All? No, Not Eve...   \n",
       "1  19551  South Carolina Stuns Baylor to Reach the Round...   \n",
       "2  25221  U.S. Presidential Race, Apple, Gene Wilder: Yo...   \n",
       "3  18026  His Predecessor Gone, Gambia’s New President F...   \n",
       "4  21063  ‘Harry Potter and the Cursed Child’ Goes From ...   \n",
       "\n",
       "                                             content  \n",
       "0  Dr. Frank Sacks, a professor of nutrition at H...  \n",
       "1  South Carolina’s win over   Duke was not only ...  \n",
       "2  (Want to get this briefing by email? Here’s th...  \n",
       "3  BANJUL, Gambia  —   A week after he was inaugu...  \n",
       "4  The biggest book of the summer isn’t a blockbu...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   id       100 non-null    int64 \n",
      " 1   title    100 non-null    object\n",
      " 2   content  100 non-null    object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sample = data.sample(20).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take just the content of the article, lowercase and remove punctuation\n",
    "articles = data_sample['content'].str.lower().apply(lambda x: re.sub(r\"([^\\w\\s])\", \"\", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stop word removal\n",
    "en_stopwords = stopwords.words('english')\n",
    "articles = articles.apply(lambda x: ' '.join([word for word in x.split() if word not in (en_stopwords)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenize\n",
    "articles = articles.apply(lambda x: word_tokenize(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stemming (done for speed as we have a lot of text)\n",
    "ps = PorterStemmer()\n",
    "articles = articles.apply(lambda tokens: [ps.stem(token) for token in tokens])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [novemb, 2000, florida, recount, grip, nation,...\n",
       "1     [tesla, motor, maverick, maker, scrutini, fede...\n",
       "2     [year, could, end, without, littl, bit, taylor...\n",
       "3     [washington, hillari, clinton, assail, donald,...\n",
       "4     [washington, russian, warplan, carri, airstrik...\n",
       "5     [alli, mangel, learn, lose, vision, rare, cond...\n",
       "6     [mose, allison, pianist, singer, songwrit, str...\n",
       "7     [nomad, wander, necessari, surviv, came, stock...\n",
       "8     [banjul, gambia, week, inaugur, anoth, countri...\n",
       "9     [coverag, leak, 2005, audio, tape, donald, tru...\n",
       "10    [within, span, last, summer, four, five, bigge...\n",
       "11    [read, friday, storm, watch, latest, updat, hu...\n",
       "12    [dhaka, bangladesh, bangladesh, govern, move, ...\n",
       "13    [least, 27, peopl, shot, seven, fatal, period,...\n",
       "14    [washington, presid, trump, plan, take, execut...\n",
       "15    [shahram, amiri, emerg, shadow, spotlight, six...\n",
       "16    [london, british, govern, announc, plan, tuesd...\n",
       "17    [student, citi, colleg, new, york, flagship, l...\n",
       "18    [beverli, hill, calif, nov, 12, bill, maher, b...\n",
       "19    [new, york, citi, subway, train, jam, capac, p...\n",
       "Name: content, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "articles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary(3659 unique tokens: ['10', '1787', '2000', '2004', '2008']...)\n"
     ]
    }
   ],
   "source": [
    "# create dictionary of all words\n",
    "dictionary = corpora.Dictionary(articles)\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vecotize using bag of words into a document term matrix\n",
    "doc_term = [dictionary.doc2bow(text) for text in articles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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1), (1109, 1), (1116, 2), (1117, 5), (1118, 1), (1119, 1), (1137, 2), (1139, 1), (1141, 1), (1144, 1), (1155, 3), (1157, 1), (1167, 1), (1178, 1), (1181, 1), (1198, 1), (1199, 1), (1203, 1), (1215, 1), (1217, 1), (1225, 1), (1232, 3), (1238, 1), (1239, 1), (1250, 1), (1287, 1), (1302, 1), (1317, 1), (1321, 1), (1332, 1), (1376, 1), (1382, 1), (1398, 2), (1421, 3), (1436, 2), (1458, 1), (1464, 3), (1503, 1), (1523, 1), (1553, 1), (1554, 2), (1569, 1), (1582, 2), (1638, 1), (1704, 1), (1710, 2), (1715, 1), (1739, 1), (1759, 4), (1819, 2), (1855, 2), (1865, 1), (1868, 1), (1944, 1), (1957, 1), (2002, 3), (2080, 2), (2081, 1), (2084, 2), (2085, 4), (2129, 1), (2132, 1), (2173, 2), (2181, 1), (2214, 2), (2234, 2), (2272, 1), (2280, 1), (2347, 1), (2370, 1), (2414, 1), (2445, 1), (2463, 1), (2464, 1), (2475, 1), (2483, 2), (2490, 1), (2498, 1), (2505, 1), (2530, 1), (2555, 1), (2565, 1), (2568, 1), (2580, 1), (2586, 2), (2600, 1), (2647, 2), (2661, 1), (2663, 1), (2697, 1), (2700, 1), (2714, 1), (2718, 6), (2746, 3), (2752, 2), (2799, 4), (2812, 2), (2829, 14), (2840, 2), (2875, 1), (2894, 1), (2928, 1), (2941, 1), (2943, 1), (2959, 1), (2980, 1), (3019, 2), (3072, 1), (3075, 1), (3155, 1), (3157, 1), (3174, 2), (3177, 2), (3185, 1), (3188, 1), (3201, 1), (3203, 1), (3211, 1), (3217, 1), (3223, 1), (3227, 1), (3231, 6), (3240, 1), (3274, 5), (3286, 1), (3341, 1), (3407, 2), (3422, 1), (3429, 1), (3449, 1), (3476, 1), (3481, 1), (3509, 1), (3510, 1), (3513, 1), (3537, 1), (3541, 1), (3542, 1), (3543, 1), (3544, 1), (3545, 1), (3546, 1), (3547, 1), (3548, 1), (3549, 2), (3550, 1), (3551, 1), (3552, 1), (3553, 1), (3554, 1), (3555, 2), (3556, 7), (3557, 1), (3558, 1), (3559, 1), (3560, 1), (3561, 1), (3562, 1), (3563, 1), (3564, 1), (3565, 1), (3566, 1), (3567, 1), (3568, 3), (3569, 1), (3570, 2), (3571, 1), (3572, 1), (3573, 1), (3574, 1), (3575, 7), (3576, 1), (3577, 1), (3578, 1), (3579, 1), (3580, 1), (3581, 1), (3582, 1), (3583, 2), (3584, 1), (3585, 3), (3586, 27), (3587, 1), (3588, 3), (3589, 1), (3590, 2), (3591, 1), (3592, 6), (3593, 1), (3594, 1), (3595, 1), (3596, 1), (3597, 1), (3598, 1), (3599, 1), (3600, 1), (3601, 12), (3602, 1), (3603, 1), (3604, 1), (3605, 2), (3606, 1), (3607, 1), (3608, 1), (3609, 1), (3610, 1), (3611, 1), (3612, 1), (3613, 1), (3614, 1), (3615, 1), (3616, 2), (3617, 1), (3618, 2), (3619, 1), (3620, 1), (3621, 1), (3622, 1), (3623, 1), (3624, 2), (3625, 1), (3626, 1), (3627, 2), (3628, 1), (3629, 1), (3630, 1), (3631, 1), (3632, 1), (3633, 2), (3634, 1), (3635, 1), (3636, 1), (3637, 1), (3638, 1), (3639, 1), (3640, 2), (3641, 1), (3642, 1), (3643, 2), (3644, 1), (3645, 1), (3646, 1), (3647, 1), (3648, 1), (3649, 1), (3650, 1), (3651, 1), (3652, 1), (3653, 2), (3654, 1), (3655, 1), (3656, 1), (3657, 1), (3658, 2)]]\n"
     ]
    }
   ],
   "source": [
    "print(doc_term)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify number of topics\n",
    "num_topics = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'random_seed'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-a4dc03d4fd8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# create LSA model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlsamodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLsiModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc_term\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_topics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid2word\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdictionary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlsamodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_topics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_topics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'random_seed'"
     ]
    }
   ],
   "source": [
    "# create LSA model\n",
    "lsamodel = LsiModel(doc_term, num_topics=num_topics, id2word = dictionary) \n",
    "print(lsamodel.print_topics(num_topics=num_topics, num_words=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine Number of Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate coherence scores to determine an optimum number of topics\n",
    "coherence_values = []\n",
    "model_list = []\n",
    "\n",
    "min_topics = 2\n",
    "max_topics = 11\n",
    "\n",
    "for num_topics_i in range(min_topics, max_topics+1):\n",
    "    model = LsiModel(doc_term, num_topics=num_topics_i, id2word = dictionary)\n",
    "    model_list.append(model)\n",
    "    coherence_model = CoherenceModel(model=model, texts=articles, dictionary=dictionary, coherence='c_v')\n",
    "    coherence_values.append(coherence_model.get_coherence())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(min_topics, max_topics+1), coherence_values)\n",
    "plt.xlabel(\"Number of Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, '0.525*\"mr\" + 0.364*\"said\" + 0.164*\"would\" + 0.154*\"trump\" + 0.140*\"state\"'), (1, '-0.603*\"leas\" + 0.328*\"mr\" + -0.273*\"vehicl\" + -0.185*\"compani\" + -0.157*\"car\"')]\n"
     ]
    }
   ],
   "source": [
    "final_n_topics = 2\n",
    "lsamodel_f = LsiModel(doc_term, num_topics=final_n_topics, id2word = dictionary) \n",
    "print(lsamodel_f.print_topics(num_topics=final_n_topics, num_words=5))"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}