https://itunes.apple.com/us/app/emotion-calculator-for-twitter/id591404584?ls=1&mt=8

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• pip install sentiment_classifier
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• pypi package
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Sentiment Classifier using Word Sense Disambiguation using wordnet and word occurance statistics from movie review corpus nltk. For twitter sentiment analysis bigrams are used as features on Naive Bayes and Maximum Entropy Classifier from the twitter data. Classifies into positive and negative labels. Next is use senses instead of tokens from the respective data.

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The above online demo uses movie review corpus from nltk, twitter and Amazon,on which Naive Bayes classifier is trained. Classifier using WSD SentiWordNet is based on heuristics and uses WordNet and SentiWordNet. Test results on sentiment analysis on twitter and amazon customer reviews data & features used for NaiveBayes will be Github.

In Version 0.5 all the following requirements are installed automatically. In case of troubles install those manually.

• You must have Python 2.6.
• NLTK https://www.nltk.org 2.0 installed.
• NumPy https://numpy.scipy.org
• SentiWordNet https://sentiwordnet.isti.cnr.it

Shell command

python setup.py install

• https://readthedocs.org/docs/sentiment_classifier/en/latest/
• Try Online

Shell Commands:

senti_classifier -c file/with/review.txt

Shell Commands

cd sentiment_classifier/src/senti_classifier/
python senti_classifier.py -c reviews.txt

from senti_classifier import senti_classifier
sentences = ['The movie was the worst movie', 'It was the worst acting by the actors']
pos_score, neg_score = senti_classifier.polarity_scores(sentences)
print pos_score, neg_score

... 0.0 1.75

from senti_classifier.senti_classifier import synsets_scores
print synsets_scores['peaceful.a.01']['pos']

... 0.25

• 0.5 No additional data required trained data is loaded automatically. Much faster/Optimized than previous versions.
• 0.4 Added Bag of Words as a Feature as occurance statistics
• 0.3 Sentiment Classifier First app, Using WSD module