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

IEEE Access

Xu Yuan¹  Fei Lu¹  Zhikui Chen¹  Fangming Zhong¹ 

[1] School of Software Technology, Dalian University of Technology, Dalian, China;

关键词: Cross-modal;    hypergraph learning;    topic model;    sentiment classification;    product reviews;   

DOI  :  10.1109/ACCESS.2017.2782668

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown