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Sentiment analysis is a common task in natural language processing that aimsto detect polarity of a text document (typically a consumer review). In thesimplest settings, we discriminate only between positive and negativesentiment, turning the task into a standard binary classification problem. Wecompare several ma- chine learning approaches to this problem, and combine themto achieve the best possible results. We show how to use for this task thestandard generative lan- guage models, which are slightly complementary to thestate of the art techniques. We achieve strong results on a well-known datasetof IMDB movie reviews. Our results are easily reproducible, as we publish alsothe code needed to repeat the experiments. This should simplify further advanceof the state of the art, as other researchers can combine their techniques withours with little effort.

Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews