kevincobain2000 / sentiment_classifier

Sentiment Classification using Word Sense Disambiguation
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machine-learning sentiment-analysis word-sense-disambiguation wsd

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

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.

.. raw:: html


sentiment_classifier-0.5.tar.gz

Download Stats Provided by pypi-github-stats <http://www.jaist.ac.jp/~s1010205/pypi-git-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 <https://github.com/kevincobain2000/sentiment_classifier>_.

Requirements

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

How to Install

Shell command ::

python setup.py install

Documentation

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

.. code-block:: python

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

.. code-block:: python

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

... 0.25

History

.. include:: run_time.rst .. include:: disqus_jnlp.html.rst