Release Time:2019-03-12  Hits:

Indexed by: Journal Article

Date of Publication: 2018-01-01

Journal: IEEE ACCESS

Included Journals: SCIE

Volume: 6

Page Number: 24856-24865

ISSN: 2169-3536

Key Words: Cross-modal; hypergraph learning; topic model; sentiment classification; product reviews

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, Naive Bayes, maximum entropy, and support vector machine. Results demonstrate that our schemes outperform the baseline methods in sentiment classification accuracy.