TCMHG: Topic-Based Cross-Modal Hypergraph Learning for Online Service Recommendations

Abstract

Online product reviews sentiment classification plays an important role on service recommendation, yet most of current researches on it only focus on single-modal information ignoring the complementary information, that results in unsatisfied accuracy of sentiment classification. This paper proposes a cross-modal hypergraph model to capture textual information and sentimental information simultaneously for sentiment classification of reviews. Furthermore, a mixture model by coupling the latent Dirichlet allocation topic model with the proposed cross-modal hypergraph is designed to mitigate the ambiguity of some specific words, which may express opposite polarity in different contexts. Experiments are carried out on four-domain data sets (books, DVD, electronics, and kitchen) to evaluate the proposed approaches by comparison with lexicon-based method, Naïve Bayes, maximum entropy, and support vector machine. Results demonstrate that our schemes outperform the baseline methods in sentiment classification accuracy.

Keywords

• Cross-modal
• hypergraph learning
• topic model
• sentiment classification
• product reviews