research-article

Latent aspect rating analysis without aspect keyword supervision

Authors:

ChengXiang ZhaiAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 618 - 626

https://doi.org/10.1145/2020408.2020505

Published: 21 August 2011 Publication History

Abstract

Mining detailed opinions buried in the vast amount of review text data is an important, yet quite challenging task with widespread applications in multiple domains. Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings. A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords. However, the aspect information is not always available, and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews.

In this paper, we propose a unified generative model for LARA, which does not need pre-specified aspect keywords and simultaneously mines 1) latent topical aspects, 2) ratings on each identified aspect, and 3) weights placed on different aspects by a reviewer. Experiment results on two different review data sets demonstrate that the proposed model can effectively perform the Latent Aspect Rating Analysis task without the supervision of aspect keywords. Because of its generality, the proposed model can be applied to explore all kinds of opinionated text data containing overall sentiment judgments and support a wide range of interesting application tasks, such as aspect-based opinion summarization, personalized entity ranking and recommendation, and reviewer behavior analysis.

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Cited By

Chen LMankad S(2024)A Structural Topic and Sentiment-Discourse Model for Text AnalysisSSRN Electronic Journal10.2139/ssrn.4020651Online publication date: 2024
https://doi.org/10.2139/ssrn.4020651
Mouratidis KLi KTang B(2024)Marrying Top-k with Skyline Queries: Operators with Relaxed Preference Input and Controllable Output SizeACM Transactions on Database Systems10.1145/370572650:1(1-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3705726
Kabir MOthman ZYaakub M(2024)A Hybrid Frequency Based, Syntax, and Conditional Random Field Method for Implicit and Explicit Aspect ExtractionIEEE Access10.1109/ACCESS.2024.340347912(72361-72373)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3403479
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Index Terms

Latent aspect rating analysis without aspect keyword supervision
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 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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Published: 21 August 2011

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August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Chen LMankad S(2024)A Structural Topic and Sentiment-Discourse Model for Text AnalysisSSRN Electronic Journal10.2139/ssrn.4020651Online publication date: 2024
https://doi.org/10.2139/ssrn.4020651
Mouratidis KLi KTang B(2024)Marrying Top-k with Skyline Queries: Operators with Relaxed Preference Input and Controllable Output SizeACM Transactions on Database Systems10.1145/370572650:1(1-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3705726
Kabir MOthman ZYaakub M(2024)A Hybrid Frequency Based, Syntax, and Conditional Random Field Method for Implicit and Explicit Aspect ExtractionIEEE Access10.1109/ACCESS.2024.340347912(72361-72373)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3403479
Rismala RUlfa Maulidevi NSurendro K(2024)Personalized Neural Network-Based Aggregation Function in Multi-Criteria Collaborative FilteringJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.101922(101922)Online publication date: Jan-2024
https://doi.org/10.1016/j.jksuci.2024.101922
Hua YDenny PWicker JTaskova K(2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10462-024-10906-z
Fan GZhang CChen JLi PLin YLeung V(2023)Improving Rating Prediction in Multi-criteria Recommender Systems via a Collective Factor ModelSSRN Electronic Journal10.2139/ssrn.4618243Online publication date: 2023
https://doi.org/10.2139/ssrn.4618243
Rismala RMaulidevi NSurendro K(2023)Multi-Attribute BERT for Preferences Completion in Multi-Criteria Recommender SystemProceedings of the 2023 12th International Conference on Software and Computer Applications10.1145/3587828.3587875(315-320)Online publication date: 23-Feb-2023
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Veldt NBenson AKleinberg J(2023)Combinatorial characterizations and impossibilities for higher-order homophilyScience Advances10.1126/sciadv.abq32009:1Online publication date: 6-Jan-2023
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Fan GZhang CChen JLi PLi YLeung V(2023)Improving Rating Prediction in Multi-Criteria Recommender Systems Via a Collective Factor ModelIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3270910(1-11)Online publication date: 2023
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Weng CHuang CChen YHuang Y(2023)New information search model for online reviews with the perspective of user requirementsMultimedia Tools and Applications10.1007/s11042-023-14847-782:18(28165-28185)Online publication date: 20-Feb-2023
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