Quantifying sentiment analysis using python

Question

I've been using NLTK in python for doing sentiment analysis, it only has positive, neutral and negative class, what if we want to do sentiment analysis and having a number to show how much a sentence can be negative or positive. Sort of seeing it as a regression problem. Is there any pre-trained library out there to do so?

Answer 1

I know of a few ways to do this:

• Vader returns score as a gradation (between zero and one)
• Stanford NLP returns a categorical classification (i.e. 0, 1, 2, 3).

An NLTK way:

from nltk.sentiment.vader import SentimentIntensityAnalyzer as sia
sentences = ['This is the worst lunch I ever had!',
             'This is the best lunch I have ever had!!',
             'I don\'t like this lunch.',
             'I eat food for lunch.',
             'Red is a color.',
             'A really bad, horrible book, the plot was .']

hal = sia()
for sentence in sentences:
    print(sentence)
    ps = hal.polarity_scores(sentence)
    for k in sorted(ps):
        print('\t{}: {:>1.4}'.format(k, ps[k]), end='  ')
    print()

Example output:

This is the worst lunch I ever had!
    compound: -0.6588   neg: 0.423      neu: 0.577      pos: 0.0  

A Stanford-NLP, Python way:

(Note that this way requires you to start an instance of the CoreNLP server to run e.g.: java -mx1g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000)

from pycorenlp import StanfordCoreNLP
stanford = StanfordCoreNLP('http://localhost:9000')

for sentence in sentences:
    print(sentence)
    result = stanford.annotate(sentence,
                               properties={
                                'annotators': 'sentiment',
                                'outputFormat': 'json',
                                'timeout': '5000'
                               })
    for s in result['sentences']:
        score = (s['sentimentValue'], s['sentiment'])
    print(f'\tScore: {score[0]}, Value: {score[1]}')

Example output:

This is the worst lunch I ever had!
    Score: 0, Value: Verynegative