Name
Title | ||
|---|---|---|
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, Naive Bayes, maximum entropy, and support vector machine. Results demonstrate that our schemes outperform the baseline methods in sentiment classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2017.2782668 | IEEE ACCESS |
Keywords | Field | DocType |
Cross-modal,hypergraph learning,topic model,sentiment classification,product reviews | Latent Dirichlet allocation,Information retrieval,Naive Bayes classifier,Computer science,Sentiment analysis,Support vector machine,Topic model,Principle of maximum entropy,Ambiguity,Mixture model,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhikui Chen | 1 | 692 | 66.76 |
| Fei Lu | 2 | 0 | 0.68 |
| Xu Yuan | 3 | 61 | 24.92 |
| Fangming Zhong | 4 | 9 | 6.57 |