Jointly modeling review content and aspect ratings for review rating prediction

Zhipeng Jin, Qiudan Li, Dajun Zeng, Yong Cheng Zhan, Ruoran Liu, Lei Wang, Hongyuan Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

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 languageEnglish (US)
Title of host publicationSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages893-896
Number of pages4
ISBN (Electronic)9781450342902
DOIs
StatePublished - Jul 7 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: Jul 17 2016Jul 21 2016

Other

Other39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
CountryItaly
CityPisa
Period7/17/167/21/16

Keywords

  • Aspect rating
  • Data missing
  • Review rating prediction
  • Skip-thought vectors

ASJC Scopus subject areas

  • Information Systems
  • Software

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