hist3368-week6-using-ngrams-to-interpret-gender.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hist 3368: Using Ngrams to Interpret Gender\n",
    "\n",
    "#### By Jo Guldi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook generates visualizations that will be used for the writing assignment this week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and clean some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk, numpy, re, matplotlib\n",
    "import pandas as pd\n",
    "import re\n",
    "from textblob import TextBlob\n",
    "from nltk.corpus import stopwords\n",
    "stopwords = stopwords.words('english')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/group/history/hist_3368-jguldi\n"
     ]
    }
   ],
   "source": [
    "cd /scratch/group/history/hist_3368-jguldi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('senseandsensibility.txt', 'r') as myfile:\n",
    "    sas_data = myfile.read().split('\\n\\n\"I suppose you know, ma\\'am, that Mr. Ferrars is married\"\\n\\nIt _was_ Edward\\n\\n\"Everything in such respectable condition\"\\n\\n ')[1].split('THE END')[0].strip()\n",
    "with open('emma.txt', 'r') as myfile:\n",
    "    emma_data = myfile.read().split('CHAPTER I')[1].split('FINIS')[0].strip()\n",
    "with open('prideandprejudice.txt', 'r') as myfile:\n",
    "    pap_data = myfile.read().split('CHAPTER I')[1].split('End of the Project Gutenberg EBook of Pride and Prejudice, by Jane Austen')[0].strip()\n",
    "data = [sas_data, emma_data, pap_data]\n",
    "\n",
    "# clean up\n",
    "for novel in data:\n",
    "    novel = re.sub('[\\\",.;:?([)\\]_*]', '', novel) # strip punctuation\n",
    "    words = novel.replace('\\n', ' ').lower().split() # remove whitespace, lowercase, split into words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Ngrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CHAPTER', 'I'],\n",
       " ['I', 'The'],\n",
       " ['The', 'family'],\n",
       " ['family', 'of'],\n",
       " ['of', 'Dashwood']]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allbigrams = []\n",
    "\n",
    "# extract ngrams where n is set at 2\n",
    "for novel in data:\n",
    "    bigrams = TextBlob(novel).ngrams(n=2) # get ngrams where n = 2\n",
    "    for bigram in bigrams: # for each bigram in list\n",
    "        bigram2 = list(bigram) # reformat the WordList bigram as a normal list\n",
    "        allbigrams.append(bigram2) # save the bigram for later\n",
    "     \n",
    "allbigrams[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### top bigrams with any word stopworded\n",
      "I am            464\n",
      "Mrs Jennings    464\n",
      "I have          406\n",
      "could not       332\n",
      "her sister      274\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "###################\n",
    "# stopword the bigram list -- two options\n",
    "# stopwording either word in the bigram\n",
    "anycleanbigramlist = []\n",
    "for bigram in allbigrams: # cycle through each bigram in the bigram list\n",
    "    for word in bigram:\n",
    "        if any(word not in stopwords for word in bigram): # if all the words in the bigram are non-stopwords, then:\n",
    "            bigram2 = ' '.join(bigram) # glue the split words back into a phrase with a space in the middle\n",
    "            anycleanbigramlist.append(bigram2) # use double brackets to grab the whole row -- including the index\n",
    "print('### top bigrams with any word stopworded')\n",
    "print(pd.Series.value_counts(anycleanbigramlist)[:5]) # show just the top 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "said he       82\n",
       "he could      56\n",
       "he would      44\n",
       "he replied    42\n",
       "he said       42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###################\n",
    "# filter stopworded lists for 'he' \n",
    "pattern = re.compile(\"\\\\bhe\\\\b\")\n",
    "\n",
    "he_bigrams = [] \n",
    "for bigram in anycleanbigramlist:\n",
    "    if re.search(pattern, bigram):\n",
    "        he_bigrams.append(bigram)\n",
    "mostcommon = pd.Series.value_counts(he_bigrams)[:55] \n",
    "mostcommon[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######\n",
    "# a well-labeled barplot\n",
    "plt.figure(figsize = (20, 5)) # make a wide and squat image\n",
    "plt.subplots_adjust(bottom=0.35) # add some padding at the bottom so that the labels don't get cut off\n",
    "plt.bar(list(mostcommon.index), mostcommon) # tell the barplot what to put on the x and y axes\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Top Bigrams With HE in Jane Austen') #This line labels the title\n",
    "plt.savefig('he-bigrams.png', dpi = 300) # save the figure\n",
    "plt.show() # show the figure below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "her sister     274\n",
       "her mother     220\n",
       "her husband     64\n",
       "her eyes        54\n",
       "her mind        44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###################\n",
    "# filter stopworded lists for 'her' as the first word \n",
    "pattern = re.compile(\"\\\\bher\\\\b\")\n",
    "\n",
    "he_bigrams = [] \n",
    "for bigram in anycleanbigramlist:\n",
    "    bigram2 = bigram.split() ### <- note the .split() \n",
    "    if re.search(pattern, bigram2[0]): ### <-- note the [0] to find the first word. change to [1] to find the second word\n",
    "            he_bigrams.append(bigram)\n",
    "mostcommon = pd.Series.value_counts(he_bigrams)[:55] # count them and take the most prevalent\n",
    "mostcommon[:5] # show the top five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######\n",
    "# a well-labeled barplot\n",
    "plt.figure(figsize = (20, 5)) # make it wide and squat\n",
    "plt.subplots_adjust(bottom=0.35) # add padding so we don't lose the labels when we save\n",
    "plt.bar(list(mostcommon.index), mostcommon) # tell the barplot what to put on the x and y axes\n",
    "plt.xticks(rotation=90) # this tells Python to 'rotate' the labels on the x axis so that the words fit on the screen\n",
    "plt.savefig('bar.png') # this line tells Python to save a copy in your digital-history folder\n",
    "plt.xlabel('Word') # this line labels the x axis\n",
    "plt.ylabel('Count') # this line labels the y axis\n",
    "plt.suptitle('Top Bigrams From the Novels of Jane Austen with HER as the First Word ') #This line labels the title\n",
    "plt.savefig('her-0-bigrams.png', dpi = 300) # save the figure in your folder\n",
    "plt.show() # display the figure below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment\n",
    "\n",
    "1) Use the code above to generate 6 graphs:\n",
    "    \n",
    "   - Two for comparing 'he' and 'she'\n",
    "   - Two for comparing 'his' and 'her' (where 'her' is used as the possessive, i.e. the first word in the bigram)\n",
    "   - Two for comparing 'him' and 'her' (where 'her' is used as direct object, or the second word in the bigram)\n",
    "\n",
    "Make sure each figure is appropriately labeled.\n",
    "\n",
    "Cut and paste the resulting graphs into a Word document. Label them so that you can refer to them (Figure 1, etc.).\n",
    "\n",
    "2) Write a series of paragraphs comparing how men and women are talked about in Jane Austen. Use all the words from all six charts in your discussion. \n",
    "\n",
    "In an essay of at least one page, citing individual words throughout, make an argument and provide detailed evidence for your answer.  You should discuss in your essay all of the words you have noted in your charts.\n",
    "\n",
    "The examples may be discussed in a list (\"Austen uses the verbs 'play,' 'sing,' and 'make merry' to talk about what the sisters did in their spare time.\")  Make sure that anything you say about a group of words is accurate for each word you talk about in that sentence. \n",
    "\n",
    "Turn in a Word Document with a clear thesis -- that is, a single argument supported by all of your data and analysis.\n",
    "\n",
    "\n"
   ]
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