def print_topics(model, feature_names, n_top_words, topic_prev):
	"""Prints the topic information. Takes the sklearn.decomposition.LatentDiricheltAllocation lda model, 
	the names of all the features, the number of words to be printined per topic, a list holding the freq
	of each topic in the corpus"""
	i = 0
	message_list =[]
	for topic_idx, topic in enumerate(model.components_):
		message = "%f Topic #%d: " % (topic_prev[i],topic_idx)
		i +=1
		list_feat = [feature_names[i]
							for i in topic.argsort()[:-n_top_words - 1:-1]]
		feat_freq = sorted(topic, reverse=True)
		for j in range(0, len(list_feat)):
			list_feat[j] += " " + str(round(feat_freq[j], 3)) + ","

		message += " ".join(list_feat)
		message_list.append(message)
		print(message)
	print()
	return message_list