Pessimists and optimists: Improving collaborative filtering through sentiment analysis

• We apply Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments.
• We have generated a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb).
• We improve Collaborative Filtering algorithms in rating prediction tasks.

This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.