Sentiment Classification using Word Sense Disambiguation, Senti Word Net and word occurance statistics using movie review corpus
Sentiment Classification using WSD
Sentiment Classifier using Word Sense Disambiguation using wordnet and word occurance statistics from movie review corpus nltk. Classifies into positive and negative categories.
In Version 0.5 all the following requirements are installed automatically. In case of troubles install those manually.
How to Install
python setup.py install
senti_classifier -c file/with/review.txt
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
- 0.6 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
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