Sentiment Analyzer

sentiment.py

import json
import nltk
nltk.download("subjectivity")
nltk.download("punkt")
nltk.download("vader_lexicon")
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import math

def main():
	n_instances = 100
	subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]]
	obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]]

	train_subj_docs = subj_docs[:80]
	test_subj_docs = subj_docs[80:100]
	train_obj_docs = obj_docs[:80]
	test_obj_docs = obj_docs[80:100]
	training_docs = train_subj_docs + train_obj_docs
	testing_docs = test_subj_docs + test_obj_docs

	sentim_analyzer = SentimentAnalyzer()
	all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])

	unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
	sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)

	training_set = sentim_analyzer.apply_features(training_docs)
	test_set = sentim_analyzer.apply_features(testing_docs)

	trainer = NaiveBayesClassifier.train
	classifier = sentim_analyzer.train(trainer, training_set)

	sub_dict = {}
	sorted_sub = {}
	f = open("sortedsubreddits.txt", "r")
	data = json.loads(f.read())
	f.close()

	for item in data:
		sub_dict[item] = [0, 0]
	
	f = open("filteredposts.txt", "r")
	posts = json.loads(f.read())
	f.close()

	sid = SentimentIntensityAnalyzer()
	for post in posts:
		if post[1] not in sub_dict:
			continue
		ss = sid.polarity_scores(post[0])
		for k in sorted(ss)[:1]:
			sub_dict[post[1]][0] += ss[k]
			sub_dict[post[1]][1] += 1

	for item in sub_dict:
		if sub_dict[item][0] == 0 or sub_dict[item][1] == 0:
			continue
		
		avg = math.floor(sub_dict[item][0] / sub_dict[item][1] * 1000) / 1000
		sorted_sub[item] = avg
	
	sorted_sub_2 = sorted(sorted_sub, key=lambda item: sorted_sub[item])

	for item in sorted_sub_2:
		f = open("results.txt", "a")
		f.write(f"{item}: {sorted_sub[item]}\n")
		f.close()
		print(f"{item}: {sorted_sub[item]}")

if __name__ == "__main__":
	main()