Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction
文献类型：会议论文
| 作者 | Zhipeng Jin1,2 ; Qiudan Li1; Daniel D. Zeng1,2,3; YongCheng Zhan3; Ruoran Liu1,2; Lei Wang1; Hongyuan Ma4
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| 出版日期 | 2016 |
| 会议名称 | Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval |
| 会议日期 | July 17-21, 2016 |
| 会议地点 | Pisa, Italy |
| 关键词 | Review Rating Prediction Aspect Rating Data Missing |
| 通讯作者 | Qiudan Li |
| 英文摘要 | 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. |
| 会议录 | Proceedings 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
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| 源URL | [http://ir.ia.ac.cn/handle/173211/12272]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 作者单位 | 1.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 |
| 推荐引用方式 GB/T 7714 | Zhipeng Jin,Qiudan Li,Daniel D. Zeng,et al. Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction[C]. 见:Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. Pisa, Italy. July 17-21, 2016. |
入库方式： OAI收割
来源：自动化研究所
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