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Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach

We describe a rating inference approach to incorporating textual user reviews into collaborative filtering (CF) algorithms. The main idea of our approach is to elicit user preferences expressed in textual reviews, a problem known as sentiment analysis , and map such preferences onto some rating scales that can be understood by existing CF algorithms. One important task in our rating inference framework is the determination of sentimental orientations (SO) and strengths of opinion words. It is because inferring a rating from a review is mainly done by extracting opinion words in the review, and then aggregating the SO of such words to determine the dominant or average sentiment implied by the user. We performed some preliminary analysis on movie reviews to investigate how SO and strengths of opinion words can be determined, and proposed a relative-frequency-based method for performing such task. The proposed method addresses a major limitation of existing methods by allowing similar words to have different SO. We also developed and evaluated a prototype of the proposed framework. Preliminary results validated the effectiveness of various tasks in the proposed framework, and suggest that the framework does not rely on a large training corpus to function. Further development of our rating inference framework is ongoing. A comprehensive evaluation of the framework will be carried out and reported in a follow-up article.