Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers

Citations

This library is sited here.

http://www.aclweb.org/anthology/W14-2708

iPhone App for Twitter Sentiments is Out

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

App no longer available. Sorry Due to lack of funds to run a seperate server App has been taken out of the app store. Use it free to build your own app tho

Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers

• pip install sentiment_classifier
• Home
• pypi package
• Github

Overview

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.

Download Stats Provided by pypi-github-stats

Sentiment Classifiers and Data

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.

Requirements

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

• You must have Python 2.6+ or Python 3.4+.
• NLTK http://www.nltk.org 2.0 installed.
• NumPy http://numpy.scipy.org
• SentiWordNet http://sentiwordnet.isti.cnr.it

How to Install

Shell command

python setup.py install

Documentation

• http://pythonhosted.org/sentiment_classifier/

Script Usage

Shell Commands:

senti_classifier -c file/with/review.txt

Python Usage

Shell Commands

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

Library Usage

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

History

• 0.7 Python 3.0 suport Thanks to @MrLokans
• 0.6 Bug Fixed upon nltk upgrade
• 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