Abstract
Sentiment analysis is a common task in natural language processing that aims
to detect polarity of a text document (typically a consumer review). In the
simplest settings, we discriminate only between positive and negative
sentiment, turning the task into a standard binary classification problem. We
compare several ma- chine learning approaches to this problem, and combine them
to achieve the best possible results. We show how to use for this task the
standard generative lan- guage models, which are slightly complementary to the
state of the art techniques. We achieve strong results on a well-known dataset
of IMDB movie reviews. Our results are easily reproducible, as we publish also
the code needed to repeat the experiments. This should simplify further advance
of the state of the art, as other researchers can combine their techniques with
ours with little effort.
Users
Please
log in to take part in the discussion (add own reviews or comments).