This library is sited here.
iPhone App for Twitter Sentiments is Out
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
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.
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
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
0.7Python 3.0 suport Thanks to @MrLokans
0.6Bug Fixed upon nltk upgrade
0.5No additional data required trained data is loaded automatically. Much faster/Optimized than previous versions.
0.4Added Bag of Words as a Feature as occurance statistics
0.3Sentiment Classifier First app, Using WSD module