You\t1\n",
+ "don't\t1\n",
+ "have\t1\n",
+ "permission\t1\n",
+ "to\t1\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": 4,
+ "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",
+ "import requests, re, nltk\n",
+ "from bs4 import BeautifulSoup\n",
+ "from nltk import clean_html\n",
+ "from collections import Counter\n",
+ "import operator\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": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#I am going to compare two of the most popular books written by H. G. Wells\n",
+ "#\"The War of the Worlds\"\n",
+ "#\"The First Men In The Moon\"\n",
+ "#Both of the above works deal with Extra terrestial sci fi adventures so they have a basis to be compared and analyzed\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 15 most frequent words in The War of the Worlds\n",
+ "us\t104\n",
+ "little\t112\n",
+ "man\t113\n",
+ "black\t117\n",
+ "could\t117\n",
+ "time\t119\n",
+ "towards\t129\n",
+ "saw\t130\n",
+ "came\t148\n",
+ "people\t159\n",
+ "martians\t159\n",
+ "said\t166\n",
+ "upon\t173\n",
+ "one\t191\n",
+ "\n",
+ " 15 most frequent words in The First Men In The Moon\n",
+ "would\t134\n",
+ "must\t140\n",
+ "like\t151\n",
+ "seemed\t153\n",
+ "upon\t157\n",
+ "could\t158\n",
+ "time\t166\n",
+ "came\t167\n",
+ "moon\t190\n",
+ "little\t200\n",
+ "cavor\t231\n",
+ "us\t239\n",
+ "one\t274\n",
+ "said\t313\n"
+ ]
+ }
+ ],
+ "source": [
+ "#compare two books from different same author\n",
+ "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",
+ "\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",
+ " number_of_words = 15\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),number_of_words)\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/36.txt.utf-8')\n",
+ "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/1013.txt.utf-8')\n",
+ "\n",
+ "num = 15 #change to match the number_of_words variable in function***********************************************\n",
+ "\n",
+ "ml = min(len(wf_ee),num)\n",
+ "rank = 1\n",
+ "\n",
+ "print(\"\", num, \"most frequent words in The War of the Worlds\")\n",
+ "ml = min(len(wf_ee),num)\n",
+ "for i in range(1,ml,1):\n",
+ " print (wf_ee[i][0]+\"\\t\"+str(wf_ee[i][1]))\n",
+ " \n",
+ "print(\"\\n\", num, \"most frequent words in The First Men In The Moon\")\n",
+ "ml = min(len(wf_bu),num)\n",
+ "for i in range(1,ml,1):\n",
+ " print (wf_bu[i][0]+\"\\t\"+str(wf_bu[i][1]))\n",
+ "\n",
+ "#for i in range(1,ml,1):\n",
+ "# print (\"#\",rank,)\n",
+ "# print (wf_ee[i][0]+\"\\t\"+str(wf_ee[i][1]), \"uses in The War of the Worlds\")\n",
+ "# print (wf_bu[i][0]+\"\\t\"+str(wf_bu[i][1]), \"uses in The First Men In The Moon\")\n",
+ "# print('\\n')\n",
+ "# rank+=1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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