Python - Text Classification





Many times, we need to categorise the available text into various categories by some pre-defined criteria. nltk provides such feature as part of various corpora. In the below example we look at the movie review corpus and check the categorization available.

# Lets See how the movies are classified
from nltk.corpus import movie_reviews

all_cats = []
for w in movie_reviews.categories():
    all_cats.append(w.lower())
print(all_cats)

When we run the above program, we get the following output −

['neg', 'pos']

Now let's look at the content of one of the files with a positive review. The sentences in this file are tokenized and we print the first four sentences to see the sample.

from nltk.corpus import movie_reviews
from nltk.tokenize import sent_tokenize
fields = movie_reviews.fileids()

sample = movie_reviews.raw("pos/cv944_13521.txt")

token = sent_tokenize(sample)
for lines in range(4):
    print(token[lines])

When we run the above program we get the following output −

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meteor threat set to blow away all volcanoes & twisters !
summer is here again !
this season could probably be the most ambitious = season this decade with hollywood churning out films 
like deep impact , = godzilla , the x-files , armageddon , the truman show , 
all of which has but = one main aim , to rock the box office .
leading the pack this summer is = deep impact , one of the first few film 
releases from the = spielberg-katzenberg-geffen's dreamworks production company .

Next, we tokenize the words in each of these files and find the most common words by using the FreqDist function from nltk.

import nltk
from nltk.corpus import movie_reviews
fields = movie_reviews.fileids()

all_words = []
for w in movie_reviews.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)
print(all_words.most_common(10))

When we run the above program we get the following output −

[(,', 77717), (the', 76529), (.', 65876), (a', 38106), (and', 35576), 
(of', 34123), (to', 31937), (u"'", 30585), (is', 25195), (in', 21822)]


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Ans: Python Text Processing Tutorial for Beginners - Learn Python Text Processing in simple and easy steps starting from basic to advanced concepts with examples including Text Processing,Text Processing Environment,String Immutability,Sorting Lines,Reformatting Paragraphs,Counting Token in Paragraphs ,Convert Binary to ASCII,Convert ASCII to Binary,Strings as Files,Backward File Reading,Filter Duplicate Words,Extract Emails from Text,Extract URL from Text,Pretty Print Numbers,Text Processing State Machine,Capitalize and Translate,Tokenization,Remove Stopwords,Synonyms and Antonyms,Text Translation,Word Replacement,Spelling Check,WordNet Interface,Corpora Access,Tagging Words,Chunks and Chinks,Chunk Classification,Text Classification,Bigrams,Process PDF,Process Word Document,Reading RSS feed,Sentiment Analysis,Search and Match,Text Munging,Text wrapping,Frequency Distribution,Text Summarization,Stemming Algorithms,Constrained Search. view more..
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Ans: Python Text Processing Tutorial for Beginners - Learn Python Text Processing in simple and easy steps starting from basic to advanced concepts with examples including Text Processing,Text Processing Environment,String Immutability,Sorting Lines,Reformatting Paragraphs,Counting Token in Paragraphs ,Convert Binary to ASCII,Convert ASCII to Binary,Strings as Files,Backward File Reading,Filter Duplicate Words,Extract Emails from Text,Extract URL from Text,Pretty Print Numbers,Text Processing State Machine,Capitalize and Translate,Tokenization,Remove Stopwords,Synonyms and Antonyms,Text Translation,Word Replacement,Spelling Check,WordNet Interface,Corpora Access,Tagging Words,Chunks and Chinks,Chunk Classification,Text Classification,Bigrams,Process PDF,Process Word Document,Reading RSS feed,Sentiment Analysis,Search and Match,Text Munging,Text wrapping,Frequency Distribution,Text Summarization,Stemming Algorithms,Constrained Search. view more..
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