Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98d92482-218c-47e7-b735-176dae27cd99",
   "metadata": {},
   "source": [
    "# Text Analysis for Digital Humanities: Project\n",
    "\n",
    "This notebook is meant to help you with your own project. \n",
    "You are not expected to understand all the code in this notebook, but feel free to [submit a consulting request](https://dlab.berkeley.edu/consulting/submit-consulting-request) with me if you want to learn more.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "<b>Yellow text</b> like this indicates there are inputs and code that you can alter, so that it points to the texts and words you want to analyze.</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28ab158c-7a87-4cc8-8a75-d06c40748b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (3.8.1)\n",
      "Requirement already satisfied: click in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from nltk) (8.1.6)\n",
      "Requirement already satisfied: joblib in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from nltk) (1.3.1)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from nltk) (2023.6.3)\n",
      "Requirement already satisfied: tqdm in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from nltk) (4.65.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/tomvannuenen/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pip install nltk\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8eadf9-b2f0-4e2b-a229-860f47fe3df1",
   "metadata": {},
   "source": [
    "## Import Data\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "Change the <b>file_path</b> variable below to point to a text you are interested in exploring!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "273a942a-dfee-4225-bcf2-d419b8cb9c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'data/1895_wilde-oscar_the-importance-of-being-earnest.txt'\n",
    "\n",
    "with open(file_path, 'r', encoding='utf-8') as file:\n",
    "    text = file.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f3e122-e615-4865-93e3-7702571fd5ba",
   "metadata": {
    "tags": []
   },
   "source": [
    "# NLTK Text objects\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "Change the inputs for the methods below (currently 'brother', 'sister' etc.) to explore words of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e12c0030-2644-4787-8bce-b3e4d704b4a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concordance for input word:\n",
      "Displaying 25 of 51 matches:\n",
      " always pretended to have a younger brother of the name of Ernest , who lives i\n",
      "have invented a very useful younger brother called Ernest , in order that you m\n",
      " accepts me , I am going to kill my brother , indeed I think I 'll kill him in \n",
      "en propose for practice . I know my brother Gerald does . All my girl-friends t\n",
      "nsense . Algernon : What about your brother ? What about the profligate Ernest \n",
      "! Jack : Very well , then . My poor brother Ernest was carried off suddenly , i\n",
      "le too much interested in your poor brother Ernest ? Wo n't she feel his loss a\n",
      "ts , that unfortunate young man his brother seems to be . But I must not distur\n",
      "4 , The Albany , W. ' Uncle Jack 's brother ! Did you tell him Mr. Worthing was\n",
      " from your card , are Uncle Jack 's brother , my cousin Ernest , my wicked cous\n",
      " some terrible calamity ? Jack : My brother . Miss Prism : More shameful debts \n",
      "ack : [ -- ] Dead ! Chasuble : Your brother Ernest dead ? Jack : Quite dead . M\n",
      "re interested , Mr. Worthing ? Your brother was , I believe , unmarried , was h\n",
      " think is in the dining-room ? Your brother ! Jack : Who ? Cecily : Your brothe\n",
      "rother ! Jack : Who ? Cecily : Your brother Ernest . He arrived about half an h\n",
      " : What nonsense ! I have n't got a brother . Cecily : Oh , do n't say that . H\n",
      " you in the past , he is still your brother . You could n't be so heartless as \n",
      " peculiarly distressing . Jack . My brother is in the dining-room ? I do n't kn\n",
      "otions Algernon away . ] Algernon . Brother John , I have come down from town t\n",
      "ou are not going to refuse your own brother 's hand ? Jack . Nothing will induc\n",
      " . But I must say that I think that Brother John 's coldness to me is peculiarl\n",
      "nfessed to us that he had a younger brother who was very wicked and bad , you o\n",
      "hing who is my guardian . It is his brother -- his elder brother . GWENDOLEN : \n",
      "an . It is his brother -- his elder brother . GWENDOLEN : [ Sitting down again \n",
      "never mentioned to me that he had a brother . CECILY : I am sorry to say they h\n",
      "\n",
      "Words similar to input word:\n",
      "name case man moment room music for but two way little pleasure county\n",
      "aunt well being doubt matter place hard\n",
      "\n",
      "Common contexts of input words:\n",
      "No common contexts were found\n",
      "\n",
      "Collocations in the text:\n",
      "Lady Bracknell; Miss Prism; Mr. Worthing; Aunt Augusta; Miss Fairfax;\n",
      "Miss Cardew; Uncle Jack; cigarette case; Dr. Chasuble; dear fellow;\n",
      "Good heavens; LADY BRACKNELL; n't think; severe chill; cucumber\n",
      "sandwiches; MISS PRISM; Lord Bracknell; need hardly; Mr. Bunbury;\n",
      "three months\n",
      "\n",
      "Dispersion plot for input words:\n"
     ]
    },
    {
     "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": [
    "from nltk.text import Text\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# Assuming 'text' is your corpus as a single string\n",
    "# Tokenize the corpus\n",
    "tokens = word_tokenize(text)\n",
    "# Create an NLTK Text object\n",
    "nltk_text = Text(tokens)\n",
    "\n",
    "# Example operations:\n",
    "# 1. Concordance\n",
    "print(\"Concordance for input word:\")\n",
    "nltk_text.concordance('brother')\n",
    "\n",
    "# 2. Similar\n",
    "print(\"\\nWords similar to input word:\")\n",
    "nltk_text.similar('brother')\n",
    "\n",
    "# 3. Common Contexts\n",
    "print(\"\\nCommon contexts of input words:\")\n",
    "nltk_text.common_contexts(['brother', 'sister'])\n",
    "\n",
    "# 4. Collocations\n",
    "print(\"\\nCollocations in the text:\")\n",
    "nltk_text.collocations()\n",
    "\n",
    "# 5. Dispersion Plot\n",
    "print(\"\\nDispersion plot for input words:\")\n",
    "nltk_text.dispersion_plot(['brother', 'mother', 'sister'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076778c7-5e78-4e7e-815b-d0934226113a",
   "metadata": {},
   "source": [
    "# Comparative stylometry using TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d510dfb-f764-4af2-92a3-41095cfc0fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import gutenberg\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [10, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c76194-ef28-4c58-9a5c-da1d83e71eb8",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Change the file paths for the <b>read_file</b> function calls below to point to texts you want to compare.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf560775-4660-46d6-ba0f-ef0adb1866d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_file(file_path):\n",
    "    with open(file_path, 'r', encoding='utf-8') as file:\n",
    "        return file.read()\n",
    "\n",
    "text1 = read_file('data/1853_boucicault-dion_grimaldi.txt')\n",
    "text2 = read_file('data/1856_boucicault-dion_the-phantom.txt')\n",
    "text3 = read_file('data/1919_yeats-wb_the-only-jealousy-of-emer.txt')\n",
    "text4 = read_file('data/1895_wilde-oscar_the-importance-of-being-earnest.txt')\n",
    "text5 = read_file('data/1895_shaw-george-bernard_you-never-can-tell.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949ae3d5-838f-40b6-8335-945d7a7d135d",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Change the list items for <b>texts</b> below to the texts you are comparing. Note that I am simply using author names.\n",
    "\n",
    "It is recommended to use at least 2 texts of the same author, so you can check if those texts are more similar.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ccca4d9-184c-482e-a290-052571428aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = ['boucicault1','boucicault2','yeats','wilde','shaw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d61ac4fa-24d0-4ad3-b76e-08b9005670fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(max_features=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75b047bd-edbe-47f8-8d3f-cbfbea92a0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_matrix = tfidf.fit_transform([text1, text2, text3, text4, text5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b96ecde-0496-436b-a7db-35bf5cc7f7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5x500 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 1986 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591ea306-4b5b-4084-b7a1-f303947aee91",
   "metadata": {},
   "source": [
    "**Sparse matrix**: Matrix (table) with some values missing    \n",
    "**Dense matrix**: That matrix filled in (think: fillna(0)). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96b94833-aaaa-497d-afbf-c72ce1a574ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidfDense = tfidf_matrix.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "947bc78b-a158-45fc-9028-59bf3c509713",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = tfidf.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb2ec057-6eef-4d3f-9945-94b92b8c49fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(tfidfDense, index=texts, columns=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b9624e6c-97d1-4e00-9c2c-071411d3b145",
   "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>about</th>\n",
       "      <th>act</th>\n",
       "      <th>ada</th>\n",
       "      <th>afraid</th>\n",
       "      <th>after</th>\n",
       "      <th>afternoon</th>\n",
       "      <th>again</th>\n",
       "      <th>against</th>\n",
       "      <th>age</th>\n",
       "      <th>ago</th>\n",
       "      <th>...</th>\n",
       "      <th>worthing</th>\n",
       "      <th>would</th>\n",
       "      <th>years</th>\n",
       "      <th>yes</th>\n",
       "      <th>yet</th>\n",
       "      <th>you</th>\n",
       "      <th>young</th>\n",
       "      <th>your</th>\n",
       "      <th>yours</th>\n",
       "      <th>yourself</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>boucicault1</th>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.020688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002821</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027042</td>\n",
       "      <td>0.004772</td>\n",
       "      <td>0.030091</td>\n",
       "      <td>0.006363</td>\n",
       "      <td>0.334045</td>\n",
       "      <td>0.009544</td>\n",
       "      <td>0.112939</td>\n",
       "      <td>0.008463</td>\n",
       "      <td>0.007158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boucicault2</th>\n",
       "      <td>0.004569</td>\n",
       "      <td>0.006752</td>\n",
       "      <td>0.167798</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011422</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.005711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015991</td>\n",
       "      <td>0.009138</td>\n",
       "      <td>0.022958</td>\n",
       "      <td>0.006853</td>\n",
       "      <td>0.211314</td>\n",
       "      <td>0.007996</td>\n",
       "      <td>0.063965</td>\n",
       "      <td>0.004051</td>\n",
       "      <td>0.001142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yeats</th>\n",
       "      <td>0.011431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013515</td>\n",
       "      <td>0.011431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025720</td>\n",
       "      <td>0.011431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005716</td>\n",
       "      <td>0.002858</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014289</td>\n",
       "      <td>0.188616</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.102881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wilde</th>\n",
       "      <td>0.035076</td>\n",
       "      <td>0.004013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010033</td>\n",
       "      <td>0.015275</td>\n",
       "      <td>0.015326</td>\n",
       "      <td>0.003960</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>0.014368</td>\n",
       "      <td>0.002829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072423</td>\n",
       "      <td>0.033944</td>\n",
       "      <td>0.006789</td>\n",
       "      <td>0.040133</td>\n",
       "      <td>0.002829</td>\n",
       "      <td>0.288527</td>\n",
       "      <td>0.010183</td>\n",
       "      <td>0.071283</td>\n",
       "      <td>0.002676</td>\n",
       "      <td>0.001131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shaw</th>\n",
       "      <td>0.018435</td>\n",
       "      <td>0.004064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.011873</td>\n",
       "      <td>0.003174</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>0.005937</td>\n",
       "      <td>0.006348</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014998</td>\n",
       "      <td>0.004999</td>\n",
       "      <td>0.038419</td>\n",
       "      <td>0.006249</td>\n",
       "      <td>0.290891</td>\n",
       "      <td>0.023434</td>\n",
       "      <td>0.062177</td>\n",
       "      <td>0.003694</td>\n",
       "      <td>0.004999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                about       act       ada    afraid     after  afternoon  \\\n",
       "boucicault1  0.007953  0.020688  0.000000  0.002821  0.003181   0.000000   \n",
       "boucicault2  0.004569  0.006752  0.167798  0.000000  0.011422   0.000000   \n",
       "yeats        0.011431  0.000000  0.000000  0.013515  0.011431   0.000000   \n",
       "wilde        0.035076  0.004013  0.000000  0.010033  0.015275   0.015326   \n",
       "shaw         0.018435  0.004064  0.000000  0.005541  0.011873   0.003174   \n",
       "\n",
       "                again   against       age       ago  ...  worthing     would  \\\n",
       "boucicault1  0.003181  0.002386  0.000000  0.005567  ...  0.000000  0.027042   \n",
       "boucicault2  0.003427  0.005711  0.000000  0.010280  ...  0.000000  0.015991   \n",
       "yeats        0.025720  0.011431  0.000000  0.002858  ...  0.000000  0.005716   \n",
       "wilde        0.003960  0.001131  0.014368  0.002829  ...  0.072423  0.033944   \n",
       "shaw         0.015622  0.005937  0.006348  0.001250  ...  0.000000  0.014998   \n",
       "\n",
       "                years       yes       yet       you     young      your  \\\n",
       "boucicault1  0.004772  0.030091  0.006363  0.334045  0.009544  0.112939   \n",
       "boucicault2  0.009138  0.022958  0.006853  0.211314  0.007996  0.063965   \n",
       "yeats        0.002858  0.000000  0.014289  0.188616  0.008573  0.102881   \n",
       "wilde        0.006789  0.040133  0.002829  0.288527  0.010183  0.071283   \n",
       "shaw         0.004999  0.038419  0.006249  0.290891  0.023434  0.062177   \n",
       "\n",
       "                yours  yourself  \n",
       "boucicault1  0.008463  0.007158  \n",
       "boucicault2  0.004051  0.001142  \n",
       "yeats        0.000000  0.002858  \n",
       "wilde        0.002676  0.001131  \n",
       "shaw         0.003694  0.004999  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7970b7b1-9182-4352-a736-3c60078ad7de",
   "metadata": {},
   "source": [
    "## Stylometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a56d9f11-5186-4adf-9ba2-b8c67de1e7fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ad7ec7c-d452-4b81-920f-52127b934fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "tfidfArray = np.asarray(tfidfDense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "847f49e3-095e-4062-9b1a-15e3b4ee2f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Normalize the TF-IDF array\n",
    "scaler = StandardScaler()\n",
    "tfidfArray_normalized = scaler.fit_transform(tfidfArray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c34cd9f2-bf29-4905-9af9-0dd42d12ab81",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcaOut = pca.fit_transform(tfidfArray_normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50628e1b-1b9c-4f73-bf2b-732d0de41a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.32602958,  13.7136309 ],\n",
       "       [  3.90322138,  12.97606612],\n",
       "       [ 22.28991201, -13.04196864],\n",
       "       [-17.67417626, -11.177337  ],\n",
       "       [ -9.84498671,  -2.47039138]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcaOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84b472c4-0aff-4550-87a7-3abdf937e495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ys = pcaOut[:,0], pcaOut[:,1]\n",
    "for i in range(len(xs)): \n",
    "    plt.scatter(xs[i], ys[i])\n",
    "    plt.annotate(texts[i], (xs[i], ys[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ddff14-a43a-483d-a0e9-3cd734db5a06",
   "metadata": {},
   "source": [
    "<a id='ner'></a>\n",
    "# Named Entity Recognition "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1e3fd121-5857-446d-804d-645a9eaab2d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting en-core-web-sm==3.6.0\n",
      "  Using cached https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.6.0/en_core_web_sm-3.6.0-py3-none-any.whl (12.8 MB)\n",
      "Requirement already satisfied: spacy<3.7.0,>=3.6.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from en-core-web-sm==3.6.0) (3.6.0)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.4)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.0.9)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.7)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.0.8)\n",
      "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.10)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.1.2)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.4.7)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.9)\n",
      "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.9.0)\n",
      "Requirement already satisfied: pathy>=0.10.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.10.2)\n",
      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (6.3.0)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (4.65.0)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.25.0)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.31.0)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (1.10.12)\n",
      "Requirement already satisfied: jinja2 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.1.2)\n",
      "Requirement already satisfied: setuptools in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (68.0.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (23.1)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.3.0)\n",
      "Requirement already satisfied: typing-extensions>=4.2.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (4.9.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.2.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2023.7.22)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.7.10)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (0.1.0)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (8.1.6)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/tomvannuenen/anaconda3/envs/dlab/lib/python3.10/site-packages (from jinja2->spacy<3.7.0,>=3.6.0->en-core-web-sm==3.6.0) (2.1.3)\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "spacy.cli.download('en_core_web_sm')\n",
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e345949a-ccbf-4b29-903d-b4d671d193a7",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Change the input of <b>nlp</b> below to the play you want to explore using named entity recognition.<br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8893c6a7-6704-483a-adb6-41362e01d953",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = nlp(text4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5bcdc69-0e80-4850-91a2-4b9e3d41c063",
   "metadata": {},
   "source": [
    "Here are some things that we can do with this object: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd73ec3c-a3d8-4685-85d2-3d2ab5e041cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cats',\n",
       " 'char_span',\n",
       " 'copy',\n",
       " 'count_by',\n",
       " 'doc',\n",
       " 'ents',\n",
       " 'extend_tensor',\n",
       " 'from_array',\n",
       " 'from_bytes',\n",
       " 'from_dict',\n",
       " 'from_disk',\n",
       " 'from_docs',\n",
       " 'from_json',\n",
       " 'get_extension',\n",
       " 'get_lca_matrix',\n",
       " 'has_annotation',\n",
       " 'has_extension',\n",
       " 'has_unknown_spaces',\n",
       " 'has_vector',\n",
       " 'is_nered',\n",
       " 'is_parsed',\n",
       " 'is_sentenced',\n",
       " 'is_tagged',\n",
       " 'lang',\n",
       " 'lang_',\n",
       " 'mem',\n",
       " 'noun_chunks',\n",
       " 'noun_chunks_iterator',\n",
       " 'remove_extension',\n",
       " 'retokenize',\n",
       " 'sentiment',\n",
       " 'sents',\n",
       " 'set_ents',\n",
       " 'set_extension',\n",
       " 'similarity',\n",
       " 'spans',\n",
       " 'tensor',\n",
       " 'text',\n",
       " 'text_with_ws',\n",
       " 'to_array',\n",
       " 'to_bytes',\n",
       " 'to_dict',\n",
       " 'to_disk',\n",
       " 'to_json',\n",
       " 'to_utf8_array',\n",
       " 'user_data',\n",
       " 'user_hooks',\n",
       " 'user_span_hooks',\n",
       " 'user_token_hooks',\n",
       " 'vector',\n",
       " 'vector_norm',\n",
       " 'vocab']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[method for method in dir(doc) if not method.startswith('_')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128b2d75-312c-4ce3-a4a2-4a4863308f52",
   "metadata": {},
   "source": [
    "## Named Entity Recognition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0495e489-79dd-4fbb-8e6e-9df8d0d7fd1d",
   "metadata": {},
   "source": [
    "Explore the types of entities in a subset of words of your text."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "575a243a-d9b6-42b3-91ad-e19784f58418",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Play around with the slicing of <b>doc</b> below to see other subsets of words in your play. Does any type of Named Entity stand out? Which would you like to explore further?<br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "82679e8b-486e-44b8-874b-c01c4909886d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lane : PERSON\n",
      "Lane : PERSON\n",
      "Lane : PERSON\n",
      "Lady : PERSON\n",
      "Bracknell : PERSON\n",
      "Lane : PERSON\n",
      "two : CARDINAL\n",
      "Lane : PERSON\n",
      "Thursday : DATE\n",
      "night : TIME\n",
      "eight : CARDINAL\n",
      "Lane : PERSON\n",
      "eight : CARDINAL\n"
     ]
    }
   ],
   "source": [
    "for word in doc[50:250]: \n",
    "    if len(word.text.strip()) > 0 and len(word.ent_type_) > 0: \n",
    "        print(word.text.strip(), ':', word.ent_type_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3e5f8e-8518-4ec3-9cc2-c18f70a17a0c",
   "metadata": {},
   "source": [
    "We can extract places like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b821bab0-2cf7-4d14-ae5c-8b44b564be09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shropshire\n",
      "Shropshire\n",
      "Gwendolen\n",
      "Gwendolen\n",
      "Gwendolen\n",
      "Cecily\n",
      "Cecily\n"
     ]
    }
   ],
   "source": [
    "for word in doc[:2000]: \n",
    "    if len(word.ent_type_) > 0: \n",
    "        if word.ent_type_ == 'GPE': \n",
    "            print(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7294b8f9-1d58-4c4d-a75f-a5f3a69b019a",
   "metadata": {},
   "source": [
    "And persons like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ba661bb-4f2b-4d52-94e1-b249e97c4e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jack\n",
      "Algy\n",
      "Aunt\n",
      "Augusta\n",
      "Jack\n",
      "Gwendolen\n",
      "Jack\n",
      "Jack\n",
      "Jack\n",
      "Jack\n",
      "Gwendolen\n",
      "Algy\n",
      "Worthing\n",
      "Lane\n",
      "Jack\n",
      "Jack\n",
      "Algernon\n",
      "Lane\n",
      "Ernest\n",
      "Jack\n",
      "Jack\n",
      "Jack\n",
      "Jack\n",
      "Algy\n",
      "Jack\n"
     ]
    }
   ],
   "source": [
    "for word in doc[1000:2000]: \n",
    "    if len(word.ent_type_) > 0: \n",
    "        if word.ent_type_ == 'PERSON': \n",
    "            print(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d54cbc-706d-43e3-aa64-9abe3ff09fff",
   "metadata": {},
   "source": [
    "And locations like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "beb11bd3-734f-47cb-a63c-222e3ad09ce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algernon\n",
      "Half\n",
      "-\n",
      "Moon\n",
      "Street\n"
     ]
    }
   ],
   "source": [
    "for word in doc[:4000]: \n",
    "    if len(word.ent_type_) > 0: \n",
    "        if word.ent_type_ == 'LOC': \n",
    "            print(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2707d9-5b71-42b7-9bf2-27ba740a6425",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "You can change <b>GPE</b> below to <b>TIME</b>, <b>GPE</b> or <b>PERSON</b> depending on which Named Entities you want to explore.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b06b82be-06d4-48ff-9897-c8c5f4e77de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shropshire\n",
      "Shropshire\n",
      "Gwendolen\n",
      "Gwendolen\n",
      "Gwendolen\n",
      "Cecily\n",
      "Cecily\n",
      "Albany\n",
      "Gwendolen\n",
      "Shropshire\n",
      "Shropshire\n",
      "Albany\n",
      "Willis\n",
      "London\n",
      "Gwendolen\n",
      "Gwendolen\n",
      "Willis\n",
      "Gwendolen\n"
     ]
    }
   ],
   "source": [
    "for word in doc[:4000]: \n",
    "    if len(word.ent_type_) > 0: \n",
    "        if word.ent_type_ == 'GPE': \n",
    "            print(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89bfd70d-ff94-414b-a4d8-4278bdabf2b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ents = [word.text for word in doc if word.ent_type_ == 'GPE']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31cb24d-b324-4d14-a431-4dc8c25c5fae",
   "metadata": {},
   "source": [
    "Let's see are all the unique Named Entities for the class you have chosen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d90d628-4bb5-4c25-9ffc-9000bc27832d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Albany',\n",
       " 'Australia',\n",
       " 'Cardew',\n",
       " 'Cecily',\n",
       " 'Egeria',\n",
       " 'Ernest',\n",
       " 'Evensong',\n",
       " 'Fairfax',\n",
       " 'Fifeshire',\n",
       " 'Gwendolen',\n",
       " 'Hertfordshire',\n",
       " 'Leamington',\n",
       " 'London',\n",
       " 'Metropolitan',\n",
       " 'Miss.',\n",
       " 'N.B.',\n",
       " 'Pagan',\n",
       " 'Paris',\n",
       " 'Shropshire',\n",
       " 'Sporran',\n",
       " 'Sussex',\n",
       " 'Willis',\n",
       " 'Woolton'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(ents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7bbcf8-8184-460b-95e3-6ca684070764",
   "metadata": {},
   "source": [
    "We can count our list of times, put this into a Pandas Series, and plot them, to try to show what kinds of times occur most often in Betteredge's narrative: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "673711b6-511e-41b8-9207-c9ea86765c67",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "pd.Series(Counter(ents))[list(set(ents))].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4e98f3-7fbd-4c77-89f5-630fc1bd938c",
   "metadata": {},
   "source": [
    "See how frequently places occur in narrative time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0b7162c0-3b95-477b-b378-ce361c2bc26c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = pd.Series(np.histogram([word.i for word in doc if word.ent_type_ == 'GPE'], bins=50)[0])\n",
    "times.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7660ca7-5f61-4264-901b-c897aa5f7a86",
   "metadata": {},
   "source": [
    "<a id='topic'></a>\n",
    "# Topic Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "847b0f06-a792-4c8a-99ec-6a702565b4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import pandas as pd\n",
    "from nltk import word_tokenize\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [10, 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a15cc0-faf9-4f44-acf3-9bcfffe1cf33",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Change the input of <b>split_text_into_chunks</b> below to the play you want to explore using topic modeling.<br>\n",
    "It is recommended to use a longer play, as this will improve the topic model.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a7f0ca78-baf4-4257-af06-6f23278b855e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_text_into_chunks(text, chunk_size=2000):\n",
    "    # Split the text into words\n",
    "    words = text.split()\n",
    "    \n",
    "    # Calculate the number of chunks\n",
    "    num_chunks = len(words) // chunk_size + (1 if len(words) % chunk_size > 0 else 0)\n",
    "    \n",
    "    # Split the words into chunks of the specified size\n",
    "    chunks = [\" \".join(words[i*chunk_size:(i+1)*chunk_size]) for i in range(num_chunks)]\n",
    "    \n",
    "    return chunks\n",
    "\n",
    "chunks = split_text_into_chunks(text4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d6a6dda5-7f34-4b63-9d0f-f20f4e95df1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "033610be-7919-4c3a-ae83-d9817a4ee944",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from gensim import corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "\n",
    "# Tokenize and preprocess documents\n",
    "def preprocess(text):\n",
    "    return [word for word in simple_preprocess(text) if word not in STOPWORDS]\n",
    "\n",
    "# Preprocess the documents\n",
    "processed_docs = [preprocess(doc) for doc in chunks]\n",
    "\n",
    "# Create a dictionary representation of the documents\n",
    "dictionary = corpora.Dictionary(processed_docs)\n",
    "\n",
    "# Create a bag-of-words model for each document\n",
    "bow_corpus = [dictionary.doc2bow(doc) for doc in processed_docs]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18a0610-f567-4fd7-93d0-328c5fb7c1e1",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Change the <b>number of topics (num_topics)</b> below (for smaller corpora like the ones you are probably using, anything between 3 and 10 topics should be as good as it gets).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "154b9107-6993-46dc-9711-c2bd048328c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import LdaModel\n",
    "\n",
    "# Number of topics\n",
    "num_topics = 3\n",
    "\n",
    "# Train the model on the bag-of-words corpus\n",
    "lda_model = LdaModel(bow_corpus, num_topics=num_topics, id2word=dictionary, passes=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d0eab7a9-8970-4849-9713-42b10f9bebe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic: 0 nWords: 0.033*\"jack\" + 0.018*\"chasuble\" + 0.016*\"miss\" + 0.015*\"prism\" + 0.012*\"cecily\" + 0.011*\"brother\" + 0.009*\"algernon\" + 0.007*\"mr\" + 0.006*\"think\" + 0.005*\"lady\"\n",
      "n\n",
      "Topic: 1 nWords: 0.032*\"algernon\" + 0.031*\"cecily\" + 0.028*\"jack\" + 0.020*\"gwendolen\" + 0.010*\"ernest\" + 0.009*\"miss\" + 0.009*\"oh\" + 0.008*\"lane\" + 0.007*\"yes\" + 0.007*\"worthing\"\n",
      "n\n",
      "Topic: 2 nWords: 0.024*\"algernon\" + 0.023*\"lady\" + 0.022*\"bracknell\" + 0.020*\"jack\" + 0.016*\"gwendolen\" + 0.014*\"cecily\" + 0.010*\"miss\" + 0.009*\"mr\" + 0.007*\"bunbury\" + 0.006*\"prism\"\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "for idx, topic in lda_model.print_topics(-1):\n",
    "    print(\"Topic: {} nWords: {}\".format(idx, topic ))\n",
    "    print(\"n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fdf64d91-afe5-45b0-b099-fba9f84c644f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Initialize an empty list to hold the topic distribution for each document\n",
    "topics_over_time = []\n",
    "\n",
    "for document_bow in bow_corpus:\n",
    "    # Get the topic distribution for the document\n",
    "    document_topics = lda_model.get_document_topics(document_bow, minimum_probability=0)\n",
    "    # Convert the topic distribution to a dictionary with topic ID as keys and probability as values\n",
    "    topic_prob_dict = {topic: prob for topic, prob in document_topics}\n",
    "    # Append the topic distribution dictionary to the list\n",
    "    topics_over_time.append(topic_prob_dict)\n",
    "\n",
    "# Create a DataFrame from the list of topic distribution dictionaries\n",
    "df_topics_over_time = pd.DataFrame(topics_over_time).fillna(0)  # Fill missing values with 0s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0f1ebfb7-07c3-4cc7-8af6-97652b1b7098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_topics_over_time.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba5c59aa-10ed-4962-9f6e-e6d902065adf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el69102114576905607961645602\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el69102114576905607961645602_data = {\"mdsDat\": {\"x\": [-0.0497600586326256, -0.02856820656142437, 0.07832826519404996], \"y\": [0.05551353973255247, -0.06651890504200904, 0.011005365309456608], \"topics\": [1, 2, 3], \"cluster\": [1, 1, 1], \"Freq\": [52.90922983122338, 32.34268978900271, 14.74808037977391]}, \"tinfo\": {\"Term\": [\"jack\", \"chasuble\", \"prism\", \"bracknell\", \"lady\", \"miss\", \"brother\", \"hand\", \"bunbury\", \"christened\", \"augusta\", \"man\", \"dr\", \"mr\", \"uncle\", \"lane\", \"perfectly\", \"think\", \"thirty\", \"oh\", \"house\", \"age\", \"got\", \"mourning\", \"change\", \"child\", \"markby\", \"poor\", \"merriman\", \"peculiarly\", \"cigarette\", \"cousin\", \"diary\", \"business\", \"read\", \"letters\", \"wicked\", \"puts\", \"romantic\", \"exactly\", \"isn\", \"country\", \"shropshire\", \"butter\", \"begins\", \"liked\", \"salver\", \"flowers\", \"clever\", \"lost\", \"boy\", \"bread\", \"play\", \"short\", \"accounts\", \"friends\", \"card\", \"brought\", \"picks\", \"ordinary\", \"lane\", \"engagement\", \"oh\", \"cecily\", \"ernest\", \"world\", \"sure\", \"merriman\", \"algernon\", \"gwendolen\", \"good\", \"like\", \"little\", \"love\", \"jack\", \"afraid\", \"yes\", \"worthing\", \"think\", \"sir\", \"case\", \"dear\", \"algy\", \"miss\", \"mr\", \"life\", \"know\", \"fact\", \"goes\", \"lady\", \"brother\", \"bracknell\", \"prism\", \"time\", \"aunt\", \"thirty\", \"markby\", \"cake\", \"muffin\", \"profile\", \"dreadful\", \"cucumbers\", \"invaluable\", \"bows\", \"manuscript\", \"approaches\", \"connected\", \"shame\", \"perambulator\", \"contained\", \"possibilities\", \"completely\", \"dine\", \"baby\", \"cynical\", \"bore\", \"kill\", \"plays\", \"farquhar\", \"programme\", \"pure\", \"english\", \"unhappy\", \"invitations\", \"harbury\", \"augusta\", \"nephew\", \"bracknell\", \"lady\", \"high\", \"moncrieff\", \"age\", \"bunbury\", \"consent\", \"christened\", \"aware\", \"moment\", \"tea\", \"muffins\", \"algernon\", \"aunt\", \"gwendolen\", \"mr\", \"thank\", \"jack\", \"cardew\", \"want\", \"miss\", \"left\", \"know\", \"hear\", \"prism\", \"cecily\", \"dear\", \"ask\", \"looks\", \"mean\", \"people\", \"time\", \"yes\", \"worthing\", \"chasuble\", \"thing\", \"think\", \"ernest\", \"perfectly\", \"mourning\", \"change\", \"unmarried\", \"days\", \"dressed\", \"charity\", \"sad\", \"noise\", \"son\", \"restored\", \"shakes\", \"scoundrel\", \"behaved\", \"blow\", \"occurred\", \"stay\", \"admirably\", \"brothers\", \"immersion\", \"sermon\", \"primitive\", \"trust\", \"reconciliation\", \"childish\", \"guest\", \"ridiculous\", \"faults\", \"shake\", \"past\", \"distressing\", \"chasuble\", \"prism\", \"brother\", \"jack\", \"miss\", \"dr\", \"clothes\", \"hand\", \"man\", \"christened\", \"mr\", \"uncle\", \"perfectly\", \"cecily\", \"think\", \"child\", \"forgive\", \"dead\", \"house\", \"practice\", \"algernon\", \"worthing\", \"poor\", \"got\", \"room\", \"yes\", \"lady\", \"ernest\", \"bracknell\", \"oh\", \"dear\", \"time\", \"bag\", \"like\", \"know\", \"gwendolen\", \"merriman\"], \"Freq\": [214.0, 38.0, 52.0, 92.0, 96.0, 86.0, 38.0, 22.0, 31.0, 13.0, 17.0, 16.0, 10.0, 63.0, 18.0, 39.0, 24.0, 48.0, 7.0, 46.0, 16.0, 12.0, 14.0, 3.0, 3.0, 7.0, 6.0, 13.0, 26.0, 4.0, 10.572914016447863, 8.852310072930155, 7.9998391696397135, 7.999512455610657, 7.143613823778082, 6.28580575232731, 6.283694444718894, 5.428344027952024, 5.428313823265208, 5.428294190218778, 13.997072364598722, 13.142410614691249, 4.571982257588275, 4.571570970436135, 4.571492438250414, 4.571309196483733, 4.5707982338651005, 4.5706240535044635, 4.565034676209223, 4.563886898110226, 8.857100536259122, 3.7146071199818604, 3.7140198905290194, 3.7138587988660023, 3.713709537371988, 3.713120294273359, 3.7128663231983836, 3.7127044764181965, 3.712590453725467, 3.711201038131946, 33.71940284219337, 7.9983500785797, 37.12822526402501, 132.296818217661, 44.84383933755209, 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0.2439516763936893, 0.10164653183070388], \"Term\": [\"accounts\", \"admirably\", \"afraid\", \"afraid\", \"afraid\", \"age\", \"age\", \"algernon\", \"algernon\", \"algernon\", \"algy\", \"algy\", \"algy\", \"approaches\", \"ask\", \"ask\", \"augusta\", \"augusta\", \"augusta\", \"aunt\", \"aunt\", \"aunt\", \"aware\", \"aware\", \"baby\", \"baby\", \"bag\", \"bag\", \"bag\", \"begins\", \"behaved\", \"blow\", \"bore\", \"bows\", \"boy\", \"boy\", \"bracknell\", \"bracknell\", \"bracknell\", \"bread\", \"brother\", \"brother\", \"brother\", \"brothers\", \"brought\", \"bunbury\", \"bunbury\", \"bunbury\", \"business\", \"butter\", \"cake\", \"card\", \"cardew\", \"cardew\", \"cardew\", \"case\", \"case\", \"case\", \"cecily\", \"cecily\", \"cecily\", \"change\", \"charity\", \"chasuble\", \"chasuble\", \"chasuble\", \"child\", \"child\", \"child\", \"childish\", \"christened\", \"christened\", \"christened\", \"cigarette\", \"clever\", \"clothes\", \"clothes\", \"completely\", \"connected\", \"consent\", \"consent\", \"contained\", \"country\", \"country\", \"cousin\", \"cucumbers\", \"cynical\", \"days\", \"dead\", \"dead\", \"dear\", \"dear\", \"dear\", \"diary\", \"dine\", \"dine\", \"distressing\", \"dr\", \"dr\", \"dr\", \"dreadful\", \"dressed\", \"engagement\", \"engagement\", \"english\", \"ernest\", \"ernest\", \"ernest\", \"exactly\", \"fact\", \"fact\", \"fact\", \"farquhar\", \"faults\", \"flowers\", \"forgive\", \"forgive\", \"friends\", \"goes\", \"goes\", \"goes\", \"good\", \"good\", \"good\", \"got\", \"got\", \"got\", \"guest\", \"gwendolen\", \"gwendolen\", \"gwendolen\", \"hand\", \"hand\", \"hand\", \"harbury\", \"hear\", \"hear\", \"high\", \"high\", \"house\", \"house\", \"house\", \"immersion\", \"invaluable\", \"invitations\", \"isn\", \"isn\", \"jack\", \"jack\", \"jack\", \"kill\", \"know\", \"know\", \"know\", \"lady\", \"lady\", \"lady\", \"lane\", \"lane\", \"left\", \"left\", \"left\", \"letters\", \"life\", \"life\", \"life\", \"like\", \"like\", \"like\", \"liked\", \"little\", \"little\", \"little\", \"looks\", \"looks\", \"looks\", \"lost\", \"love\", \"love\", \"man\", \"man\", \"man\", \"manuscript\", \"markby\", \"mean\", \"mean\", \"mean\", \"merriman\", \"merriman\", \"merriman\", \"miss\", \"miss\", \"miss\", \"moment\", \"moment\", \"moment\", \"moncrieff\", \"moncrieff\", \"mourning\", \"mr\", \"mr\", \"mr\", \"muffin\", \"muffins\", \"muffins\", \"nephew\", \"nephew\", \"noise\", \"occurred\", \"oh\", \"oh\", \"oh\", \"ordinary\", \"past\", \"peculiarly\", \"peculiarly\", \"people\", \"people\", \"people\", \"perambulator\", \"perfectly\", \"perfectly\", \"perfectly\", \"picks\", \"play\", \"plays\", \"poor\", \"poor\", \"poor\", \"possibilities\", \"practice\", \"practice\", \"primitive\", \"prism\", \"prism\", \"prism\", \"profile\", \"programme\", \"pure\", \"puts\", \"read\", \"reconciliation\", \"restored\", \"ridiculous\", \"romantic\", \"room\", \"room\", \"room\", \"sad\", \"salver\", \"scoundrel\", \"sermon\", \"shake\", \"shakes\", \"shame\", \"short\", \"shropshire\", \"sir\", \"sir\", \"sir\", \"son\", \"stay\", \"sure\", \"sure\", \"tea\", \"tea\", \"thank\", \"thank\", \"thing\", \"thing\", \"thing\", \"think\", \"think\", \"think\", \"thirty\", \"time\", \"time\", \"time\", \"trust\", \"uncle\", \"uncle\", \"uncle\", \"unhappy\", \"unmarried\", \"want\", \"want\", \"wicked\", \"world\", \"world\", \"worthing\", \"worthing\", \"worthing\", \"yes\", \"yes\", \"yes\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 3, 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_el69102114576905607961645602\", ldavis_el69102114576905607961645602_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el69102114576905607961645602\", ldavis_el69102114576905607961645602_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el69102114576905607961645602\", ldavis_el69102114576905607961645602_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "1     -0.049760  0.055514       1        1  52.90923\n",
       "2     -0.028568 -0.066519       2        1  32.34269\n",
       "0      0.078328  0.011005       3        1  14.74808, topic_info=           Term        Freq       Total Category  logprob  loglift\n",
       "194        jack  214.000000  214.000000  Default  30.0000  30.0000\n",
       "993    chasuble   38.000000   38.000000  Default  29.0000  29.0000\n",
       "275       prism   52.000000   52.000000  Default  28.0000  28.0000\n",
       "47    bracknell   92.000000   92.000000  Default  27.0000  27.0000\n",
       "198        lady   96.000000   96.000000  Default  26.0000  26.0000\n",
       "...         ...         ...         ...      ...      ...      ...\n",
       "715         bag    3.327113   15.566151   Topic3  -5.8924   0.3711\n",
       "207        like    3.949461   37.326974   Topic3  -5.7210  -0.3321\n",
       "197        know    3.951636   39.915195   Topic3  -5.7204  -0.3986\n",
       "161   gwendolen    4.570743  131.495452   Topic3  -5.5749  -1.4452\n",
       "1091   merriman    3.326453   26.795398   Topic3  -5.8926  -0.1723\n",
       "\n",
       "[233 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "5         1  0.956148   accounts\n",
       "1209      3  0.499574  admirably\n",
       "14        1  0.841541     afraid\n",
       "14        2  0.076504     afraid\n",
       "14        3  0.076504     afraid\n",
       "...     ...       ...        ...\n",
       "394       2  0.240920   worthing\n",
       "394       3  0.120460   worthing\n",
       "398       1  0.650538        yes\n",
       "398       2  0.243952        yes\n",
       "398       3  0.101647        yes\n",
       "\n",
       "[295 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 3, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim_models as gensimvis\n",
    "import pyLDAvis\n",
    "\n",
    "# Prepare visualization\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = gensimvis.prepare(lda_model, bow_corpus, dictionary)\n",
    "\n",
    "# Display the visualization\n",
    "vis"
   ]
  }
 ],
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