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 tas...

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Bibliographic Details
Published in:Expert systems with applications 2013-12, Vol.40 (17), p.6758-6765
Main Authors: García-Cumbreras, Miguel Á., Montejo-Ráez, Arturo, Díaz-Galiano, Manuel C.
Format: Article
Language:English
Subjects:
Affectivity. Emotion
Algorithms
Applied sciences
Biological and medical sciences
Collaborative filtering
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Filtering
Filtration
Fundamental and applied biological sciences. Psychology
IMDb corpus
Internet
Memory organisation. Data processing
Opinion mining
Personality. Affectivity
Polarity classification
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Ratings
Recommender systems
Sentiment analysis
Software
Texts
User profile generation
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Summary:•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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.06.049