def get_stats(self, topic):
        consumer_key = 'FQvMGcXfTBW085e9p8HrBGG03'
        consumer_secret = 'EyELEm6tZsgLK3HDuLYOVAtiKprCPNzx2YxOfllBvV3xZG5KSs'
        access_token = '4165420933-biz0Ne5cAEiMm7FivgPfPRu4oCIBYrf0UXOpAKI'
        access_token_secret = '6SYcS2b5zzek72huIqEK6njL1nz5cKtlFI0pBzuU4tiWx'

        auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
        auth.set_access_token(access_token, access_token_secret)

        api = tweepy.API(auth)

        results = api.search(q=topic, rpp = 100)

        # init stats
        pos = 0
        neg = 0
        for t in results:
            score = textblob.TextBlob(t.text).sentiment.polarity
            if score > 0:
                pos += 1
            elif score < 0:
                neg += 1
        return float(pos)/(pos+neg)
Returns the type with the highest probability for the given document.
dataset/ETHPy150Open LargePanda/LearnPy/learnpy/tools/TweetSentiment.py/TweetSentiment.get_stats--crossover with--dataset/ETHPy150Open clips/pattern/pattern/vector/__init__.py/IGTree.classify