test.py

import answer
import get_tweets
import operator

def refactor_sentiment(result):
	if result == "+":
		return "positive"
	elif result == "-":
		return "negative"
	else:
		return "neutral"

def main():
	n = answer.NB()
	n.learn_cpd("+", open("data/train_pos.txt"))
	n.learn_cpd("-", open("data/train_neg.txt"))
	while True:
		classify_string = raw_input("Type a string to classify!\n If you want to exit, type \"exit\" without the quotes.\n If you want to see it work on live tweets, type \"twitter\" without the quotes.\n If you want to see the classifier's features, type \"features\" without the quotes.\n If you want to see the classifier's conditional probability distributions, type \"cpds\" without the quotes.\n")
		if classify_string == "exit":
			break
		elif classify_string == "features":
			print "features:"
			print ""
			for feature in n.features:
				print "\t", feature
			#print n.features
			print ""
		elif classify_string == "cpds":
			print "cpds:"
			print ""
			for cpd in n.cpds:
				if cpd == "+":
					print "positive:"
				elif cpd == "-":
					print "negative:"
				sorted_cpds = sorted(n.cpds[cpd].iteritems(), key=operator.itemgetter(1), reverse=True)
				for elem in sorted_cpds:
					print "\t", elem[0], ": ", elem[1]
				print ""
			#print n.cpds
			print ""
		elif classify_string == "twitter":
			print ""
			tweets = get_tweets.get_tweets()
			for tweet in tweets:
				result = refactor_sentiment(n.classify(tweet))
				print "The tweet is: ", tweet
				print "\tSentiment is: ", result
			print ""
		else:
			result = refactor_sentiment(n.classify(classify_string))
			print "This string is: ", result, "\n"

if __name__ == "__main__":
	main()