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nltk_python.py
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nltk_python.py
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#Author:
#Dilpreet Singh Chawla
#Indian Institute of Information Technology Kalyani
#Using Natural Language Toolkit (nltk) in python
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from collections import Counter
#Reading text from file
fr=open('sample.txt','r')
s=fr.read()
fr.close()
s1=s.lower() #converting everything to lowercase
#Removing symbols
for symbol in ['.' , ',' , '-' , '"' , '(' , ')' , '/' , '!', ':' , ';','?',]:
s1=s1.replace(symbol,' ')
#Removing stopwords (eg: 'is', 'am', 'the')
stop_words=set(stopwords.words('english'))
word_tokens=word_tokenize(s1)
filtered_sentence =[w for w in word_tokens if not w in stop_words]
#print(filtered_sentence)
#Stemming the words (eg: changing 'running' to 'run')
stemmer=PorterStemmer()
stemmed_sentence = [stemmer.stem(w) for w in filtered_sentence]
#print(stemmed_sentence)
#counting frequency of each word
counts=dict(Counter(filtered_sentence))
#print(counts)
#Checking the type of document - Sports or Political
try:
if counts['sports']==max(counts.values()):
print("Sports Document")
fw=open("Sports.txt","w")
fw.write(s)
fw.close()
except KeyError:
print("No Sports Document")
try:
if counts['politics']==max(counts.values()):
print("Political Document")
fw=open("Politics.txt","w")
fw.write(s)
fw.close()
except KeyError:
print("No Political Document")