from nltk.tokenize import sent_tokenize
from nltk.sentiment.vader import SentimentIntensityAnalyzer

from textblob import TextBlob

def vader_analyze(text):
	sentences = []
	sentences = sent_tokenize(text)
	
	sent_sum = 0
	
	sia = SentimentIntensityAnalyzer()
	
	for sentence in sentences:
		ss = sia.polarity_scores(sentence)
		sent_sum += ss['compound']
	
	if (len(sentences) > 0):
		vader_average = sent_sum/len(sentences)
		return vader_average
	else:	
		return sent_sum
		

def blob_analyze(text):
	sentences = []
	sentences = sent_tokenize(text)
	
	sent_sum = 0
	
	for sentence in sentences:
		tb = TextBlob(sentence)
		sent_sum += tb.sentiment.polarity
	
	if (len(sentences) > 0):
		blob_average = sent_sum/len(sentences)
		return blob_average
	else:
		return sent_sum
ad6e3e49668937afb38d66571d60e66e2cf872ab
<mask token>

<mask token>


def vader_analyze(text):
    sentences = []
    sentences = sent_tokenize(text)
    sent_sum = 0
    sia = SentimentIntensityAnalyzer()
    for sentence in sentences:
        ss = sia.polarity_scores(sentence)
        sent_sum += ss['compound']
    if len(sentences) > 0:
        vader_average = sent_sum / len(sentences)
        return vader_average
    else:
        return sent_sum


<mask token>

<mask token>


def vader_analyze(text):
    sentences = []
    sentences = sent_tokenize(text)
    sent_sum = 0
    sia = SentimentIntensityAnalyzer()
    for sentence in sentences:
        ss = sia.polarity_scores(sentence)
        sent_sum += ss['compound']
    if len(sentences) > 0:
        vader_average = sent_sum / len(sentences)
        return vader_average
    else:
        return sent_sum


def blob_analyze(text):
    sentences = []
    sentences = sent_tokenize(text)
    sent_sum = 0
    for sentence in sentences:
        tb = TextBlob(sentence)
        sent_sum += tb.sentiment.polarity
    if len(sentences) > 0:
        blob_average = sent_sum / len(sentences)
        return blob_average
    else:
        return sent_sum

from nltk.tokenize import sent_tokenize
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from textblob import TextBlob


def vader_analyze(text):
    sentences = []
    sentences = sent_tokenize(text)
    sent_sum = 0
    sia = SentimentIntensityAnalyzer()
    for sentence in sentences:
        ss = sia.polarity_scores(sentence)
        sent_sum += ss['compound']
    if len(sentences) > 0:
        vader_average = sent_sum / len(sentences)
        return vader_average
    else:
        return sent_sum


def blob_analyze(text):
    sentences = []
    sentences = sent_tokenize(text)
    sent_sum = 0
    for sentence in sentences:
        tb = TextBlob(sentence)
        sent_sum += tb.sentiment.polarity
    if len(sentences) > 0:
        blob_average = sent_sum / len(sentences)
        return blob_average
    else:
        return sent_sum

from nltk.tokenize import sent_tokenize
from nltk.sentiment.vader import SentimentIntensityAnalyzer

from textblob import TextBlob

def vader_analyze(text):
	sentences = []
	sentences = sent_tokenize(text)
	
	sent_sum = 0
	
	sia = SentimentIntensityAnalyzer()
	
	for sentence in sentences:
		ss = sia.polarity_scores(sentence)
		sent_sum += ss['compound']
	
	if (len(sentences) > 0):
		vader_average = sent_sum/len(sentences)
		return vader_average
	else:	
		return sent_sum
		

def blob_analyze(text):
	sentences = []
	sentences = sent_tokenize(text)
	
	sent_sum = 0
	
	for sentence in sentences:
		tb = TextBlob(sentence)
		sent_sum += tb.sentiment.polarity
	
	if (len(sentences) > 0):
		blob_average = sent_sum/len(sentences)
		return blob_average
	else:
		return sent_sum