Sentiment analysis for sentences- positive, negative and neutral
Question: I'm designing a text classifier in Python using NLTK. One of the features
considered in every sentence is it's sentiment. I want to weight sentences
with either positive or negative sentiments more that those without any
sentiment(neutral sentences). Using the movie review corpus along with the
naive bayes classifier results in only positive and negative labels. I tried
using demo_liu_hu_lexicon in nltk.sentiment.utils but the function does not
return any values but instead prints it to the output and is very slow. Does
anyone know of a library which gives some sort of weight to sentences based on
sentiment?

Thanks!


Answer: Try the [textblob module](https://pypi.python.org/pypi/textblob):

    
    
    from textblob import TextBlob
    text = '''
    These laptops are horrible but I've seen worse. How about lunch today? The food was okay.
    '''
    
    blob = TextBlob(text)
    for sentence in blob.sentences:
        print(sentence.sentiment.polarity)
    # -0.7
    # 0.0
    # 0.5
    

It uses the nltk library to determine the polarity - which is a float measure
ranging from -1 to 1 for the sentiment. Neutral sentences have zero polarity.
You should be able to get the same measure directly from nltk.

