Predicting future reviews: sentiment analysis models for collaborative filtering

• Authors: Noriaki Kawamae
• Venue: Proceedings of the fourth ACM international conference on Web search and data mining
• Year: 2011

This paper presents hierarchical topic models for integrating sentiment analysis with collaborative filtering. Our goal is to automatically predict future reviews to a given author from previous reviews. For this goal, we focus on differentiating author's preference, while previous sentiment analysis models process these review articles without this difference. Therefore, we propose a Latent Evaluation Topic model (LET) that infer each author's preference by introducing novel latent variables into author and his/her document layer. Because these variables distinguish the variety of words in each article by merging similar word distributions, LET incorporates the difference of writers' preferences into sentiment analysis. Consequently LET can determine the attitude of writers, and predict their reviews based on like-minded writers' reviews in the collaborative filtering approach. Experiments on review articles show that the proposed model can reduce the dimensionality of reviews to the low-dimensional set of these latent variables, and is a significant improvement over standard sentiment analysis models and collaborative filtering algorithms.