Jointly Modeling Review Content and Aspect Ratings  for Review Rating Prediction

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction
	Zhipeng Jin1,2 ; Qiudan Li1 ; Daniel D. Zeng1,2,3 ; YongCheng Zhan 3; Ruoran Liu1,2 ; Lei Wang1 ; Hongyuan Ma 4
	2016
会议名称	Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
会议录名称	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
会议日期	July 17-21, 2016
会议地点	Pisa, Italy
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.
关键词	Review Rating Prediction Aspect Rating Data Missing
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12272
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Qiudan Li
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
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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],2016.

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