2-Relations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# =-=-=-=-=-=-=-=-=-=-=\n",
    "# Inline & High Resolution Graphs\n",
    "# =-=-=-=-=-=-=-=-=-=-= \n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.rcParams['figure.dpi'] = 240"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# =-=-=-=-=-=-=-=-=-=-=\n",
    "# Imports\n",
    "# =-=-=-=-=-=-=-=-=-=-= \n",
    "import re, nltk\n",
    "import pandas as pd\n",
    "from stop_words import get_stop_words\n",
    "stopwords = get_stop_words('en')\n",
    "\n",
    "# =-=-=-=-=-=-=-=-=-=-=\n",
    "# Ingest and listify the text\n",
    "# =-=-=-=-=-=-=-=-=-=-=\n",
    "mdg = open('texts/most_dangerous_game.txt', 'r').read()\n",
    "words = re.sub(\"[^'a-zA-Z']\",\" \", mdg).lower().split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stop_words import get_stop_words\n",
    "\n",
    "stopwords = get_stop_words('en')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = nltk.Text(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x1920 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text.dispersion_plot([\"rainsford\", \"general\", \"said\", \"hunting\", \"night\", \"eyes\", \"zaroff\", \"heard\", \"hunt\", \"man\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collocation Networks\n",
    "\n",
    "8-word window (4L, 4R) with stopwords removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Word Collocation Network](./files/collocation_network.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Words in Context (KWiC)\n",
    "\n",
    "A variety of words can be inserted. E.g., knife (below), but also jungle, night, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 7 of 7 matches:\n",
      "ply of food and an excellent hunting knife i give him three hours' start i am t\n",
      "pply you with hunting clothes food a knife i suggest you wear moccasins they le\n",
      "ath containing a long bladed hunting knife his right hand rested on a cocked re\n",
      " his sack of food rainsford took his knife from its sheath and began to work wi\n",
      "ng and to it he fastened his hunting knife with the blade pointing down the tra\n",
      "opped too they must have reached the knife he shinned excitedly up a tree and l\n",
      "ill on his feet but ivan was not the knife driven by the recoil of the springin\n"
     ]
    }
   ],
   "source": [
    "text.concordance(\"knife\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 22 of 22 matches:\n",
      "ng to peer through the dank tropical night that was palpable as it pressed its \n",
      "s or so through a moonless caribbean night nor four yards admitted rainsford ug\n",
      "nother pipe up on the afterdeck good night then rainsford see you at breakfast \n",
      "ford see you at breakfast right good night whitney there was no sound in the ni\n",
      "ht whitney there was no sound in the night as rainsford sat there but the muffl\n",
      "brier the sensuous drowsiness of the night was on him it's so dark he thought t\n",
      "ld sleep without closing my eyes the night would be my eyelids an abrupt sound \n",
      "hey were blotted out entirely by the night rainsford remembered the shots they \n",
      "on the rocks before he saw them on a night less calm he would have been shatter\n",
      " or a loose stone but making headway night was beginning to settle down on the \n",
      "litary mustache were as black as the night from which rainsford had come his ey\n",
      "y tent with a splitting headache one night when a terrible thought pushed its w\n",
      "laimed the general pointing into the night rainsford's eyes saw only blackness \n",
      "eral they are let out at seven every night if anyone should try to get into my \n",
      "black he looks resourceful well good night mr rainsford i hope you have a good \n",
      " well i am worried mr rainsford last night i detected traces of my old complain\n",
      "lained the hunting was not good last night the fellow lost his head he made a s\n",
      "hall not follow till dusk hunting at night is so much more exciting than by day\n",
      "x hunt and all the dodges of the fox night found him leg weary with hands and f\n",
      " general was a devil an apprehensive night crawled slowly by like a wounded sna\n",
      " follow a trail through the woods at night he could follow an extremely difficu\n",
      "nd of feet on the soft earth and the night breeze brought him the perfume of th\n"
     ]
    }
   ],
   "source": [
    "text.concordance(\"night\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modeling\n",
    "\n",
    "Divide the text into 100-word chunks, build a topic model, and then, following Colby (1963) and Schmidt's \"plot arceology\" determine which topics are dominant in the segments, dividing the segments into 10 groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Break text as list into 100-word chunks\n",
    "# Inelegant and inefficient for larger data sets\n",
    "# but easy to understand and use here\n",
    "\n",
    "chunks = [words[x:x+100] for x in range(0, len(words), 100)]\n",
    "\n",
    "# print(chunks[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "no_features = 1000\n",
    "\n",
    "# NMF is able to use tf-idf\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(preprocessor = ' '.join, \n",
    "                                   max_df = 0.95, \n",
    "                                   min_df = 2, \n",
    "                                   max_features = no_features, \n",
    "                                   stop_words = stopwords)\n",
    "tfidf = tfidf_vectorizer.fit_transform(chunks)\n",
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "575"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tfidf_feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import NMF\n",
    "\n",
    "no_topics = 10\n",
    "\n",
    "# Run NMF\n",
    "nmf = NMF(n_components=no_topics, random_state=1, alpha=.1, l1_ratio=.5, init='nndsvd').fit(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:\n",
      "general said rainsford hunting will\n",
      "Topic 1:\n",
      "stopped tree rainsford nerve reached\n",
      "Topic 2:\n",
      "sound heard night strokes rainsford\n",
      "Topic 3:\n",
      "man rainsford hand black eyes\n",
      "Topic 4:\n",
      "open lights window room towers\n",
      "Topic 5:\n",
      "sometimes window feel sea ship\n",
      "Topic 6:\n",
      "whitney jaguar ve fear rainsford\n",
      "Topic 7:\n",
      "bed excellent better never rainsford\n",
      "Topic 8:\n",
      "pit stakes feet foot coming\n",
      "Topic 9:\n",
      "following play cat lore ridge\n"
     ]
    }
   ],
   "source": [
    "def display_topics(model, feature_names, no_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic %d:\" % (topic_idx))\n",
    "        print(\" \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]]))\n",
    "\n",
    "no_top_words = 5\n",
    "display_topics(nmf, tfidf_feature_names, no_top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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