Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction
Zhipeng Jin1,2; Qiudan Li1; Daniel D. Zeng1,2,3; YongCheng Zhan3; Ruoran Liu1,2; Lei Wang1; Hongyuan Ma4
2016
Conference NameProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Source PublicationProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016. ACM 2016, ISBN 978-1-4503-4069-4
Conference DateJuly 17-21, 2016
Conference PlacePisa, Italy
AbstractReview 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.
KeywordReview Rating Prediction Aspect Rating Data Missing
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12272
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorQiudan Li
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Department of Management Information Systems, University of Arizona, Tucson, Arizona, USA
4.CNCERT/CC, Beijing, China
Recommended Citation
GB/T 7714
Zhipeng Jin,Qiudan Li,Daniel D. Zeng,et al. Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction[C],2016.
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