textclassify.py

'''
Text Classifier
available classifiers in Texblob.classifies:
	DecisionTreeClassifier 
	MaxEntClassifier 
	NaiveBayesClassifier 
	PositiveNaiveBayesClassifier 
'''
#dataset from : https://www.kaggle.com/marklvl/sentiment-labelled-sentences-data-set/home

'''
def format_data(): 
	#---This funtion is used to label sentences and should be ran once
	import pandas as pd
	import pickle  
	training_data=[]
	dataset =pd.read_csv('data.csv')
	content=dataset['content']
	value=dataset['value']
	text_list=[]
	value_list=[]
	training_data=[]
	for text  in content:
		text_list.append(text)

	for review in value:
		value_list.append(str(review))
		n=0
	while n<len(text_list):	
		training_data.append((text_list[n],value[n])) #-- Creating turples 
													  #-- because textblob classify want require them in this form
		n=n+1
	pickle.dump(training_data,open('training_data.p','wb'))
	return training_data
'''

from textblob.classifiers import NaiveBayesClassifier
import pickle 
def classify_text(text_to_classify):
        train=pickle.load(open('training_data.p','rb'))
        TESS_classify=NaiveBayesClassifier(train)
        sentiment=(TESS_classify.classify(text_to_classify))
        return sentiment



while True:
        user=input('Enter some text and i will tell you if it is positive or not. Bear in mind i do not understand sarcasm yet > ').lower()
        if user=='q':
                print('exiting')
                break
        else:
                if classify_text(user)==1:
                        print('positive sentence')
                elif classify_text(user)==0 :
                        print('negative sentence')