Fake_Real_Classification.py

import random
import nltk
import collections
list_ = []
words = []
documents = []
SingleDocument = [[], ""]
for num in range(1, 2000):
    list_ = []
    with open("Real_Fake_News/fake" + "/" + str(num) + ".txt", 'r') as f:
        for line in f:
            for word in line.split():
                list_.append(word)
                words.append(word)
        SingleDocument = (list_, "fake")
        documents.append(SingleDocument)
for num in range(1, 2000):
    list_ = []
    with open("Real_Fake_News/real" + "/" + str(num) + ".txt", 'r') as f:
        for line in f:
            for word in line.split():
                list_.append(word)
                words.append(word)
        SingleDocument = (list_, "real")
        documents.append(SingleDocument)
print(len(documents))
random.shuffle(documents)
print(len(documents))
print(len(words))
all_words = nltk.FreqDist(words)
print(all_words)
word_features = list(all_words.keys())[:2500]
def document_features(document):
    document_words = set(document)
    features = {}
    for word in word_features:
        features['contains({})'.format(word)] = (word in document_words)
    return features
featuresets = [(document_features(d), c) for (d, c) in documents]
train_set= featuresets[:3000]
test_set = featuresets[3000:]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print("NaiveBayesClassifier Accuracy     =>"+str(nltk.classify.accuracy(classifier, test_set)*100))
classifier.show_most_informative_features(5)


classifier = nltk.DecisionTreeClassifier.train(train_set,binary=False, entropy_cutoff=0.4, depth_cutoff=10, support_cutoff=20)
print("DecisionTreeClassifier Accuracy     =>"+str(nltk.classify.accuracy(classifier, test_set)*100))
# To Test This Application Put in File 1.txt and try to make the text large as possible because the features not large (small data set)
#InputList=[]
#with open("1.txt", 'r') as f:
#    for line in f:
#        for word in line.split():
#            InputList.append(word)
#            words.append(word)
#print(classifier.classify(document_features(InputList)))