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research-article

Coarse-to-fine review selection via supervised joint aspect and sentiment model

Authors:

Peng ChengAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 617 - 626

https://doi.org/10.1145/2600428.2609570

Published: 03 July 2014 Publication History

Abstract

Online reviews are immensely valuable for customers to make informed purchase decisions and for businesses to improve the quality of their products and services. However, customer reviews grow exponentially while varying greatly in quality. It is generally very tedious and difficult, if not impossible, for users to read though the huge amount of review data. Fortunately, review quality evaluation enables a system to select the most helpful reviews for users' decision-making. Previous studies predict only the overall review utility about a product, and often focus on developing different data features to learn a quality function for addressing the problem. In this paper, we aim to select the most helpful reviews not only at the product level, but also at a fine-grained product aspect level. We propose a novel supervised joint aspect and sentiment model (SJASM), which is a probabilistic topic modeling framework that jointly discovers aspects and sentiments guided by a review helpfulness metric. One key advantage of SJASM is its ability to infer the underlying aspects and sentiments, which are indicative of the helpfulness of a review. We validate SJASM using publicly available review data, and our experimental results demonstrate the superiority of SJASM over several competing models.

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Cited By

Li QZhao CYu TWu JLi S(2023)Clustering of conversational bandits with posterior sampling for user preference learning and elicitationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09358-x33:5(1065-1112)Online publication date: 6-Mar-2023
https://doi.org/10.1007/s11257-023-09358-x
Verma SKumar ASharan A(2023)MuCon: Multi-channel convolution for targeted sentiment classificationMultimedia Tools and Applications10.1007/s11042-023-16586-183:10(28615-28633)Online publication date: 6-Sep-2023
https://doi.org/10.1007/s11042-023-16586-1
Wu JZhao CYu TLi JLi SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Clustering of Conversational Bandits for User Preference Learning and ElicitationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482328(2129-2139)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482328
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Index Terms

Coarse-to-fine review selection via supervised joint aspect and sentiment model
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

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Ministry of Education - Singapore

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

Acceptance Rates

SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Li QZhao CYu TWu JLi S(2023)Clustering of conversational bandits with posterior sampling for user preference learning and elicitationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09358-x33:5(1065-1112)Online publication date: 6-Mar-2023
https://doi.org/10.1007/s11257-023-09358-x
Verma SKumar ASharan A(2023)MuCon: Multi-channel convolution for targeted sentiment classificationMultimedia Tools and Applications10.1007/s11042-023-16586-183:10(28615-28633)Online publication date: 6-Sep-2023
https://doi.org/10.1007/s11042-023-16586-1
Wu JZhao CYu TLi JLi SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Clustering of Conversational Bandits for User Preference Learning and ElicitationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482328(2129-2139)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482328
Jnoub NAl Machot FKlas W(2020)A Domain-Independent Classification Model for Sentiment Analysis Using Neural ModelsApplied Sciences10.3390/app1018622110:18(6221)Online publication date: 8-Sep-2020
https://doi.org/10.3390/app10186221
A. Waghmare KK. Bhala S(2020)Effective Methodology for Co-Referential Aspect Based Sentiment Analysis of Tourist ReviewsInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT2062149(523-529)Online publication date: 30-Apr-2020
https://doi.org/10.32628/CSEIT2062149
Li GChen QZheng BHung NZhou PLiu G(2020)Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure MethodsACM Transactions on the Web10.1145/337554814:2(1-26)Online publication date: 7-Feb-2020
https://dl.acm.org/doi/10.1145/3375548
Yang YWu BLi LWang S(2020)A Joint Model for Aspect-Category Sentiment Analysis with TextGCN and Bi-GRU2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC50466.2020.00031(156-163)Online publication date: Jul-2020
https://doi.org/10.1109/DSC50466.2020.00031
Tian NXu YLi Y(2020)Review selection based on content qualityKnowledge and Information Systems10.1007/s10115-020-01474-zOnline publication date: 21-May-2020
https://doi.org/10.1007/s10115-020-01474-z
Basha SRajput D(2019)A roadmap towards implementing parallel aspect level sentiment analysisMultimedia Tools and Applications10.1007/s11042-018-7093-zOnline publication date: 7-Jan-2019
https://doi.org/10.1007/s11042-018-7093-z
Fu XLuo XGuo Y(2019)Entity emotion mining in social media environmentConcurrency and Computation: Practice and Experience10.1002/cpe.533631:20Online publication date: 14-Jun-2019
https://doi.org/10.1002/cpe.5336
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