Abstract
Review rating prediction is of much importance for sentiment analysis and business intelligence. Existing methods work well when aspect-opinion pairs can be accurately extracted from review texts and aspect ratings are complete. The challenges of improving prediction accuracy are how to capture the semantics of review content and how to fill in the missing values of aspect ratings. In this paper, we propose a novel review rating prediction method, which improves the prediction accuracy by capturing deep semantics of review content and alleviating data missing problem of aspect ratings. The method firstly learns the latent vector representation of review content using skip-thought vectors, a state-of-the-art deep learning method, then, the missing values of aspect ratings are filled in based on users' history reviewing behaviors, finally, a novel optimization framework is proposed to predict the review rating. Experimental results on two real-world datasets demonstrate the efficacy of the proposed method.
Original language | English (US) |
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Title of host publication | SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Publisher | Association for Computing Machinery, Inc |
Pages | 893-896 |
Number of pages | 4 |
ISBN (Electronic) | 9781450342902 |
DOIs | |
State | Published - Jul 7 2016 |
Event | 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy Duration: Jul 17 2016 → Jul 21 2016 |
Other
Other | 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 |
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Country | Italy |
City | Pisa |
Period | 7/17/16 → 7/21/16 |
Keywords
- Aspect rating
- Data missing
- Review rating prediction
- Skip-thought vectors
ASJC Scopus subject areas
- Information Systems
- Software