Note: I don't provide personal support for custom changes in the code. Only for the release. For people just starting, I recommend Treehouse for online-learning.
This code reproduces performance of the NB-SVM on the IMDB reviews from the paper:
Sida Wang and Christopher D. Manning: Baselines and Bigrams: Simple, Good Sentiment and Topic Classification; ACL 2012. http://nlp.stanford.edu/pubs/sidaw12_simple_sentiment.pdf
They obtain 91.22% while this code obtains 91.55% with bigrams and 91.82% with trigrams. Little improvements (+0.33% with bigrams and +0.6% with unigrams) versus the paper.
To reproduce the results:
git clone git@github.com:mesnilgr/nbsvm.git
cd nbsvm; chmod +x oh_my_go.sh
./oh_my_go.sh
End to end (downloading the data, tokenizing, training the models), this will take 68 mins. Note that most of the time is spent dowloading and tokenizing. Once the data has been downloaded and tokenized, training an NB-SVM only takes ~2 mins for uni+bigrams and <5 mins for uni+bi+trigrams.
Naive Bayes SVM by Grégoire Mesnil is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Based on a work at https://github.com/mesnilgr/nbsvm.
