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import nltk | |
from nltk.tokenize import word_tokenize | |
from nltk.stem import WordNetLemmatizer | |
import numpy as np | |
import pickle | |
import random | |
from collections import Counter | |
lemmatizer = WordNetLemmatizer() | |
hm_lines = 1000000 | |
def create_lexicon(pos, neg): | |
lexicon = [] | |
for fi in [pos, neg]: | |
with open(fi, 'r') as f: | |
contents = f.readlines() | |
for l in contents[:hm_lines]: | |
all_words = word_tokenize(l.lower()) | |
lexicon += list(all_words) | |
lexicon = [lemmatizer.lemmatize(i) for i in lexicon] | |
w_counts = Counter(lexicon) | |
l2 = [] | |
for w in w_counts: | |
if 1000 > w_counts[w] > 50: | |
l2.append(w) | |
return l2 | |
def sample_handling(sample, lexicon, classification): | |
featureset = [] | |
with open(sample, 'r') as f: | |
contents = f.readlines() | |
for l in contents[:hm_lines]: | |
current_words = word_tokenize(l.lower()) | |
current_words = [lemmatizer.lemmatize(i) for i in current_words] | |
features = np.zeros(len(lexicon)) | |
for word in current_words: | |
if word.lower() in lexicon: | |
index_value = lexicon.index(word.lower()) | |
features[index_value] += 1 | |
features = list(features) | |
featureset.append([features, classification]) | |
return featureset | |
def create_feature_sets_and_labels(pos, neg, test_size=0.1): | |
lexicon = create_lexicon(pos, neg) | |
features = [] | |
features += sample_handling('pos.txt', lexicon, [1, 0]) | |
features += sample_handling('neg.txt', lexicon, [0, 1]) | |
random.shuffle(features) | |
features = np.array(features) | |
testing_size = int(test_size * len(features)) | |
train_x = list(features[:, 0])[:-testing_size] | |
train_y = list(features[:, 1])[:-testing_size] | |
test_x = list(features[:, 0])[-testing_size:] | |
test_y = list(features[:, 1])[-testing_size:] | |
return train_x, train_y, test_x, test_y | |
if __name__ == '__main__': | |
train_x, train_y, test_x, test_y = create_feature_sets_and_labels('pos.txt', 'neg.txt') | |
with open('sentiment_set.pickle', 'wb') as f: | |
pickle.dump([train_x, train_y, test_x, test_y], f) |
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