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from bs4 import BeautifulSoup
import requests
import pandas as pd
url = "https://www.akc.org/reg/dogreg_stats.cfm"
r=requests.get(url)
data= r.text
soup = BeautifulSoup(data)	
	
table = soup.find_all('table')[1] 
rows = table.find_all('tr')

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import glob
import math
line=''
s=set()
flist=glob.glob(r'E:\PROGRAMMING\PYTHON\programs\corpus2\*.txt') #get all the files from the d`#open each file >> tokenize the content >> and store it in a set
for fname in flist:         
    tfile=open(fname,"r")
    line=tfile.read() # read the content of file and store in "line"
    tfile.close() # close the file
    s=s.union(set(line.split(' '))) # union of common words

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import math
from text.blob import TextBlob as tb

def tf(word, blob):
    return blob.words.count(word) / len(blob.words)

def n_containing(word, bloblist):
    return sum(1 for blob in bloblist if word in blob)

def idf(word, bloblist):

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# Given a list of words, remove any that are
# in a list of stop words.

def removeStopwords(wordlist, stopwords):
    return [w for w in wordlist if w not in stopwords]

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import glob
import math
line=''
s=set()
flist=glob.glob(r'E:\PROGRAMMING\PYTHON\programs\corpus2\*.txt') #get all the files from the d`#open each file >> tokenize the content >> and store it in a set
for fname in flist:         
    tfile=open(fname,"r")
    line=tfile.read() # read the content of file and store in "line"
    tfile.close() # close the file
    s=s.union(set(line.split(' '))) # union of common words

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from string import digits
import nltk
from nltk import word_tokenize
from nltk.corpus import stopwords
import glob
import math
import csv
import string
from collections import Counter

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library(twitteR)
library(tm)
library(wordcloud)
library(RColorBrewer)
 
midnight <- searchTwitter("#MakeAMovieSmarter",  n = 1000)
midnight_text = sapply(midnight, function(x) x$getText())
midnight_corpus = Corpus(VectorSource(midnight_text))
 
tdm = TermDocumentMatrix(

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doaLMG

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Calculator

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Twitch

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