Python and Sentiment Analysis

The following is my source code in Python. The purpose of the code is to count the total number of words in a text file and to also count the number of positive and negative words separately. This is what I get after hitting run F5 on my computer:

Counter() Positive: 0.0 Negative: 0.0

import nltk.classify.util from nltk.classify import NaiveBayesClassifier from nltk.corpus import names

from itertools import chain from glob import glob from collections import Counter

def word_feats(words): return dict([(word, True) for word in words]) positive_vocab = [ 'awesome', 'outstanding', 'fantastic', 'terrific', 'good', 'nice', 'great', ':)' ] negative_vocab = [ 'bad', 'terrible','useless', 'hate', ':(' ] neutral_vocab = [ 'movie','the','sound','was','is','actors','did','know','words','not' ] positive_features = [(word_feats(pos), 'pos') for pos in positive_vocab] negative_features = [(word_feats(neg), 'neg') for neg in negative_vocab] neutral_features = [(word_feats(neu), 'neu') for neu in neutral_vocab] train_set = negative_features + positive_features + neutral_features classifier = NaiveBayesClassifier.train(train_set) # Predict neg = 0 pos = 0 readFile = open("why I chose you love poem milad.txt","r+").read()

lines = [line.lower() for line in readFile] with open('why I chose you love poem milad.txt', 'w') as out: out.writelines(sorted(lines))

lines = [line.split(' ') for line in readFile] with open('why I chose you love poem milad.txt', 'w') as out: out.writelines(sorted(lines))

for word in readFile.split(): classResult = classifier.classify(word_feats(word)) if classResult == 'neg': neg = neg + 1 if classResult == 'pos': pos = pos + 1 wordcount = 0

wordcount = Counter(readFile)

print(wordcount)

print('Positive: ' + str(float(pos))) print('Negative: ' + str(float(neg)))