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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Written text as operational data\n",
+ "\n",
+ "Written text is one type of data\n",
+ "\n",
+ "### Why people write?\n",
+ "\n",
+ " - To communicate: their thoughts, feelings, urgency, needs, information\n",
+ "\n",
+ "### Why people communicate?\n",
+ "\n",
+ "1. To express emotions\n",
+ "1. To share information\n",
+ "1. To enable or elicit an action\n",
+ "1. ...\n",
+ "\n",
+ "### We will use written text for the purpose other than \n",
+ "1. To experience emotion\n",
+ "1. To learn something the author intended us to learn\n",
+ "1. To do what the author intended us to do\n",
+ "\n",
+ "### Instead, we will use written text to recognize who wrote it\n",
+ " - By calculating and comparing word frequencies in written documents\n",
+ " \n",
+ "See, for example, likely fictional story https://medium.com/@amuse/how-the-nsa-caught-satoshi-nakamoto-868affcef595"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example 1. Dictionaries in python (associative arrays)\n",
+ "\n",
+ "Plot the frequency distribution of words on a web page."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "class=\"menu-item\t54\n",
+ "
\t38\n",
+ "
\t35\n",
+ "\t28\n",
+ " \t21\n",
+ "\t21\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests, re\n",
+ "# re is a module for regular expressions: to detect various combinations of characters\n",
+ "import operator\n",
+ "\n",
+ "# Start from a simple document\n",
+ "r = requests .get('http://eecs.utk.edu')\n",
+ "\n",
+ "# What comes back includes headers and other HTTP stuff, get just the body of the response\n",
+ "t = r.text\n",
+ "\n",
+ "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n",
+ "wds = re.split('\\s+',t)\n",
+ "\n",
+ "# now populate a dictionary (wf)\n",
+ "wf = {}\n",
+ "for w in wds:\n",
+ " if w in wf: wf [w] = wf [w] + 1\n",
+ " else: wf[w] = 1\n",
+ "\n",
+ "# dictionaries can not be sorted, so lets get a sorted *list* \n",
+ "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n",
+ "\n",
+ "# lets just have no more than 15 words \n",
+ "ml = min(len(wfs),15)\n",
+ "for i in range(1,ml,1):\n",
+ " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example 2\n",
+ "\n",
+ "Lots of markup in the output, lets remove it --- \n",
+ "\n",
+ "use BeautifulSoup and nltk modules and practice some regular expressions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import requests, re, nltk\n",
+ "from bs4 import BeautifulSoup\n",
+ "from nltk import clean_html\n",
+ "from collections import Counter\n",
+ "import operator\n",
+ "\n",
+ "# we may not care about the usage of stop words\n",
+ "stop_words = nltk.corpus.stopwords.words('english') + [\n",
+ " 'ut', '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n",
+ " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n",
+ "\n",
+ "# We most likely would like to remove html markup\n",
+ "def cleanHtml (html):\n",
+ " from bs4 import BeautifulSoup\n",
+ " soup = BeautifulSoup(html, 'html.parser')\n",
+ " return soup .get_text()\n",
+ "\n",
+ "# We also want to remove special characters, quotes, etc. from each word\n",
+ "def cleanWord (w):\n",
+ " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n",
+ " # but we need to escape ' with \\'\n",
+ " # any character between the brackets [] is to be removed \n",
+ " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n",
+ " # get rid of numbers\n",
+ " return re.sub('^[0-9\\.]*$', \"\", wn)\n",
+ " \n",
+ "# define a function to get text/clean/calculate frequency\n",
+ "def get_wf (URL):\n",
+ " # first get the web page\n",
+ " r = requests .get(URL)\n",
+ " \n",
+ " # Now clean\n",
+ " # remove html markup\n",
+ " t = cleanHtml (r .text) .lower()\n",
+ " \n",
+ " # split string into an array of words using any sequence of spaces \"\\s+\" \n",
+ " wds = re .split('\\s+',t)\n",
+ " \n",
+ " # remove periods, commas, etc stuck to the edges of words\n",
+ " for i in range(len(wds)):\n",
+ " wds [i] = cleanWord (wds [i])\n",
+ " \n",
+ " # If satisfied with results, lets go to the next step: calculate frequencies\n",
+ " # We can write a loop to create a dictionary, but \n",
+ " # there is a special function for everything in python\n",
+ " # in particular for counting frequencies (like function table() in R)\n",
+ " wf = Counter (wds)\n",
+ " \n",
+ " # Remove stop words from the dictionary wf\n",
+ " for k in stop_words:\n",
+ " wf. pop(k, None)\n",
+ " \n",
+ " #how many regular words in the document?\n",
+ " tw = 0\n",
+ " for w in wf:\n",
+ " tw += wf[w] \n",
+ " \n",
+ " \n",
+ " # Get ordered list\n",
+ " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n",
+ " ml = min(len(wfs),15)\n",
+ "\n",
+ " #Reverse the list because barh plots items from the bottom\n",
+ " return (wfs [ 0:ml ] [::-1], tw)\n",
+ " \n",
+ "# Now populate two lists \n",
+ "(wf_ee, tw_ee) = get_wf('http://www.gutenberg.org/ebooks/1342.txt.utf-8')\n",
+ "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/76.txt.utf-8')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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\nCF5HRCRWvP322zRq1IiDBw8CsGHDBsyMm2++uSTNPffcw0UXXQTAmjV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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot the results: are there striking differences in language?\n",
+ "import numpy as np\n",
+ "import pylab\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline\n",
+ "def plotTwoLists (wf_ee, wf_bu, title):\n",
+ " f = plt.figure (figsize=(10, 6))\n",
+ " # this is painfully tedious....\n",
+ " f .suptitle (title, fontsize=20)\n",
+ " ax = f.add_subplot(111)\n",
+ " ax .spines ['top'] .set_color ('none')\n",
+ " ax .spines ['bottom'] .set_color ('none')\n",
+ " ax .spines ['left'] .set_color ('none')\n",
+ " ax .spines ['right'] .set_color ('none')\n",
+ " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n",
+ "\n",
+ " # Create two subplots, this is the first one\n",
+ " ax1 = f .add_subplot (121)\n",
+ " plt .subplots_adjust (wspace=.5)\n",
+ "\n",
+ " pos = np .arange (len(wf_ee)+1) \n",
+ " ax1 .tick_params (axis='both', which='major', labelsize=14)\n",
+ " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n",
+ " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n",
+ "\n",
+ " ax2 = f .add_subplot (122)\n",
+ " ax2 .tick_params (axis='both', which='major', labelsize=14)\n",
+ " pos = np .arange (len(wf_bu)+1) \n",
+ " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n",
+ " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n",
+ "\n",
+ "plotTwoLists (wf_ee, wf_bu, 'Difference between Pride and Prejudice and Huck Finn')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "and\t2836\n",
+ "of\t2676\n",
+ "to\t2646\n",
+ "a\t2217\n",
+ "in\t1422\n",
+ "his\t1205\n",
+ "he\t928\n",
+ "that\t920\n",
+ "was\t823\n",
+ "for\t798\n",
+ "with\t797\n",
+ "as\t672\n",
+ "I\t505\n",
+ "you\t497\n"
+ ]
+ }
+ ],
+ "source": [
+ "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n",
+ "#Assuming the file name you copy is pg4680.txt here is how you change the script\n",
+ "# Please note the option errors='replace'\n",
+ "# without it python invariably runs into unicode errors\n",
+ "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n",
+ " \n",
+ "# What comes back includes headers and other HTTP stuff, get just the body of the response\n",
+ "t = f.read()\n",
+ "\n",
+ "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n",
+ "wds = re.split('\\s+',t)\n",
+ "\n",
+ "# now populate a dictionary (wf)\n",
+ "wf = {}\n",
+ "for w in wds:\n",
+ " if w in wf: wf [w] = wf [w] + 1\n",
+ " else: wf [w] = 1\n",
+ "\n",
+ "# dictionaries can not be sorted, so lets get a sorted *list* \n",
+ "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n",
+ "\n",
+ "# lets just have no more than 15 words \n",
+ "ml = min(len(wfs),15)\n",
+ "for i in range(1,ml,1):\n",
+ " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Assignment 1\n",
+ "\n",
+ "1. Compare word frequencies between two works of a single author.\n",
+ "1. Compare word frequencies between works of two authors.\n",
+ "1. Are there some words preferred by one author but used less frequently by another author?\n",
+ "\n",
+ "Extra credit\n",
+ "\n",
+ "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n",
+ "\n",
+ "Project Gutenberg is a good source of for fiction and non-fiction.\n",
+ "\n",
+ "E.g below are two most popular books from Project Gutenberg:\n",
+ "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n",
+ "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Single Author"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt \n",
+ "import requests, re\n",
+ "from bs4 import BeautifulSoup\n",
+ "from collections import Counter\n",
+ "\n",
+ "def frequency (all_articles):\n",
+ " freqs = [] \n",
+ " for i in range(len(all_articles)):\n",
+ " c = Counter(all_articles[i])\n",
+ " freqs.append(c)\n",
+ " return freqs\n",
+ "\n",
+ "def parse(articles):\n",
+ " articles_words = [] \n",
+ " remove = {\n",
+ " \"with\": 0,\"the\": 0,\"a\": 0,\"on\": 0,\"of\": 0,\"to\": 0,\"and\": 0,\"an\": 0,\"for\": 0,\n",
+ " }\n",
+ "\n",
+ " for book in articles:\n",
+ " r = requests.get(book)\n",
+ " soup = BeautifulSoup(r.text, 'html.parser')\n",
+ " text = soup.get_text().lower()\n",
+ " words = re.split('\\s+', text)\n",
+ "\n",
+ " for i in range(len(words)):\n",
+ "\n",
+ " words[i] = words[i].strip()\n",
+ "\n",
+ " f_words = [word for word in words if word not in remove]\n",
+ " \n",
+ " articles_words.append(f_words)\n",
+ " return articles_words\n",
+ "\n",
+ "def graph(a_1, a_2): \n",
+ " fig, (ax1, ax2) = plt.subplots(1,2) \n",
+ " fig.suptitle(\"iPhone 14 vs iPhone 14 Pro\")\n",
+ " ax1.barh([wordCount[0] for wordCount in a_1], [wordCount[1] for wordCount in a_1], color='red')\n",
+ " ax2.barh([wordCount[0] for wordCount in a_2], [wordCount[1] for wordCount in a_2], color='blue')\n",
+ " ax1.set_title('iPhone 14')\n",
+ " ax2.set_title('iPhone 14 Pro') \n",
+ " ax1.set(xlabel='Words', ylabel='Frequency')\n",
+ " ax2.set(xlabel='Words', ylabel='Frequency')\n",
+ " plt.show()\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " articles = []\n",
+ " a_1 = 'https://www.techradar.com/reviews/iphone-14-hands-on'\n",
+ " a_2 = 'https://www.techradar.com/reviews/iphone-14-pro' \n",
+ " articles.append(a_1)\n",
+ " articles.append(a_2)\n",
+ " all_words = parse(articles)\n",
+ " freqs = frequency(all_words)\n",
+ " graph(freqs[0].most_common(25), freqs[1].most_common(25))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1323653"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "2. Different Author"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt \n",
+ "import requests, re\n",
+ "from bs4 import BeautifulSoup\n",
+ "from collections import Counter\n",
+ "\n",
+ "def frequency (all_articles):\n",
+ " freqs = [] \n",
+ " for i in range(len(all_articles)):\n",
+ " c = Counter(all_articles[i])\n",
+ " freqs.append(c)\n",
+ " return freqs\n",
+ "\n",
+ "def parse(articles):\n",
+ " articles_words = [] \n",
+ " remove = {\n",
+ " \"with\": 0,\"the\": 0,\"a\": 0,\"on\": 0,\"of\": 0,\"to\": 0,\"and\": 0,\"an\": 0,\"for\": 0,\n",
+ " }\n",
+ "\n",
+ " for book in articles:\n",
+ " r = requests.get(book)\n",
+ " soup = BeautifulSoup(r.text, 'html.parser')\n",
+ " text = soup.get_text().lower()\n",
+ " words = re.split('\\s+', text)\n",
+ "\n",
+ " for i in range(len(words)):\n",
+ "\n",
+ " words[i] = words[i].strip()\n",
+ "\n",
+ " f_words = [word for word in words if word not in remove]\n",
+ " \n",
+ " articles_words.append(f_words)\n",
+ " return articles_words\n",
+ "\n",
+ "def graph(a_1, a_2): \n",
+ " fig, (ax1, ax2) = plt.subplots(1,2) \n",
+ " fig.suptitle(\"iPhone 14 Pro vs iPhone 14 Pro\")\n",
+ " ax1.barh([wordCount[0] for wordCount in a_1], [wordCount[1] for wordCount in a_1], color='red')\n",
+ " ax2.barh([wordCount[0] for wordCount in a_2], [wordCount[1] for wordCount in a_2], color='blue')\n",
+ " ax1.set_title('iPhone 14 Pro (Tom)')\n",
+ " ax2.set_title('iPhone 14 Pro (Tech)') \n",
+ " ax1.set(xlabel='Words', ylabel='Frequency')\n",
+ " ax2.set(xlabel='Words', ylabel='Frequency')\n",
+ " plt.show()\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " articles = []\n",
+ " a_1 = 'https://www.tomsguide.com/opinion/iphone-14-pro-and-iphone-14-pro-max-reasons-to-buy-and-skip'\n",
+ " a_2 = 'https://www.techradar.com/reviews/iphone-14-pro' \n",
+ " articles.append(a_1)\n",
+ " articles.append(a_2)\n",
+ " all_words = parse(articles)\n",
+ " freqs = frequency(all_words)\n",
+ " graph(freqs[0].most_common(25), freqs[1].most_common(25))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "3. Are there some words preferred by one author but used less frequently by another author?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "There are not some words that are preferred by one author but not the other since the topic is the same - iPhone 14 Pro. Most of the words are similar since the iPhone 14 Pro has the same specifications and functions regardless of who reviews the device."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}