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Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction

Published: 07 July 2016 Publication History

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.

References

[1]
Ochi, M., Okabe, M., and Onai, R., 2011. Rating prediction using feature words extracted from customer reviews. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 1205--1206.
[2]
Horrigan, J.A., 2008. Online shopping. Pew Internet & American Life Project Report.
[3]
Ganu, G., Elhadad, N., and Marian, A., 2009. Beyond the Stars: Improving Rating Predictions using Review Text Content. In WebDB. Citeseer, 1--6.
[4]
Diao, Q., Qiu, M., Wu, C.-Y., Smola, A.J., Jiang, J., and Wang, C., 2014. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 193--202.
[5]
Mukherjee, S., Basu, G., and Joshi, S., 2013. Incorporating author preference in sentiment rating prediction of reviews. In Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, 47--48.
[6]
Lecun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature. 521, 7553, 436--444. DOI= http://dx.doi.org/10.1038/nature14539.
[7]
Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., and Fidler, S., 2015. Skip-thought vectors. In Advances in neural information processing systems, 3276--3284.
[8]
Soley-Bori, M., 2013. Dealing with missing data: Key assumptions and methods for applied analysis. Boston University.
[9]
Le, Q.V. and Mikolov, T., 2014. Distributed representations of sentences and documents. In Proceedings of the international conference on machine learning(ICML).

Cited By

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  • (2023)TAG: Joint Triple-Hierarchical Attention and GCN for Review-Based Social Recommender SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.319495235:10(9904-9919)Online publication date: 1-Oct-2023
  • (2022)Jointly Modeling Aspect Information and Ratings for Review Rating PredictionElectronics10.3390/electronics1121353211:21(3532)Online publication date: 29-Oct-2022
  • (2022)Spider Taylor-ChOA: Optimized Deep Learning Based Sentiment Classification for Review Rating PredictionApplied Sciences10.3390/app1207321112:7(3211)Online publication date: 22-Mar-2022
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  1. Jointly Modeling Review Content and Aspect Ratings for Review Rating Prediction

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    cover image ACM Conferences
    SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
    July 2016
    1296 pages
    ISBN:9781450340694
    DOI:10.1145/2911451
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 07 July 2016

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    Author Tags

    1. aspect rating
    2. data missing
    3. review rating prediction
    4. skip-thought vectors

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    • Short-paper

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    • Important National Science & Technology Specific Project
    • NNSFC

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    SIGIR '16
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    SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2023)TAG: Joint Triple-Hierarchical Attention and GCN for Review-Based Social Recommender SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.319495235:10(9904-9919)Online publication date: 1-Oct-2023
    • (2022)Jointly Modeling Aspect Information and Ratings for Review Rating PredictionElectronics10.3390/electronics1121353211:21(3532)Online publication date: 29-Oct-2022
    • (2022)Spider Taylor-ChOA: Optimized Deep Learning Based Sentiment Classification for Review Rating PredictionApplied Sciences10.3390/app1207321112:7(3211)Online publication date: 22-Mar-2022
    • (2022)Optimal hierarchical attention network-based sentiment analysis for movie recommendationSocial Network Analysis and Mining10.1007/s13278-022-00954-012:1Online publication date: 18-Sep-2022
    • (2022)Integrating reviews and ratings into graph neural networks for rating predictionJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03626-714:7(8703-8723)Online publication date: 24-Feb-2022
    • (2021)Review text based rating prediction approaches: preference knowledge learning, representation and utilizationArtificial Intelligence Review10.1007/s10462-020-09873-y54:2(1171-1200)Online publication date: 1-Feb-2021
    • (2021)Multi-criteria and Review-Based Overall Rating PredictionAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75765-6_38(473-484)Online publication date: 11-May-2021
    • (2020)Understanding and Predicting Users’ Rating BehaviorINFORMS Journal on Computing10.1287/ijoc.2019.091932:4(996-1011)Online publication date: 1-Oct-2020
    • (2020)Personalized Review Recommendation based on Users’ Aspect SentimentACM Transactions on Internet Technology10.1145/341484120:4(1-26)Online publication date: 6-Oct-2020
    • (2020)Cold-start Point-of-interest Recommendation through CrowdsourcingACM Transactions on the Web10.1145/340718214:4(1-36)Online publication date: 25-Aug-2020
    • Show More Cited By

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