Sentiment Classification using Word Sense Disambiguation
Switch branches/tags
Nothing to show
Pull request Compare This branch is 41 commits behind kevincobain2000:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
docs
scripts
src
.gitignore
LICENSE.txt
MANIFEST.in
README
README.rst
setup.py

README.rst


Back to Home

Sentiment Classification using WSD

Overview

Sentiment Classifier using Word Sense Disambiguation using wordnet and word occurance statistics from movie review corpus nltk. Classifies into positive and negative categories.


sentiment_classifier-0.5.tar.gz

Online Demo

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

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

  • 0.5 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