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Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid

Authors:

Wayne Xin Zhao,

Xiaoming LiAuthors Info & Claims

EMNLP '10: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

Pages 56 - 65

Published: 09 October 2010 Publication History

Abstract

Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and opinions. Recently topic models have been used to identify meaningful review aspects, but existing topic models do not identify aspect-specific opinion words. In this paper, we propose a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words. We show that with a relatively small amount of training data, our model can effectively identify aspect and opinion words simultaneously. We also demonstrate the domain adaptability of our model.

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Cited By

Zhao RGui LHe YChen HDuh WHuang HKato MMothe JPoblete B(2023)Cone: Unsupervised Contrastive Opinion ExtractionProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591650(1066-1075)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591650
Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
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Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid
1. Computing methodologies
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cover image DL Hosted proceedings

EMNLP '10: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

October 2010

1332 pages

Program Chairs:
Hang Li
Microsoft Research Asia
,
Lluís Màrquez
Technical University of Catalonia

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 09 October 2010

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Overall Acceptance Rate 73 of 234 submissions, 31%

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Cited By

Zhao RGui LHe YChen HDuh WHuang HKato MMothe JPoblete B(2023)Cone: Unsupervised Contrastive Opinion ExtractionProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591650(1066-1075)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591650
Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Wang WPan S(2020)Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion ExtractionComputational Linguistics10.1162/coli_a_0036245:4(705-736)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1162/coli_a_00362
Zhang XQiao ZAhuja AFan WFox EReddy C(2019)Discovering Product Defects and Solutions from Online User Generated ContentsThe World Wide Web Conference10.1145/3308558.3313732(3441-3447)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313732
Hou YYang NWu YYu P(2019)Explainable recommendation with fusion of aspect informationWorld Wide Web10.1007/s11280-018-0558-122:1(221-240)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11280-018-0558-1
Nawaz AAsghar SNaqvi S(2019)A segregational approach for determining aspect sentiments in social media analysisThe Journal of Supercomputing10.1007/s11227-018-2664-375:5(2584-2602)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11227-018-2664-3
Maurya AAkoglu LKrishnan RBay D(2018)A lens into employee peer reviews via sentiment-aspect modelingProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382372(670-677)Online publication date: 28-Aug-2018
https://dl.acm.org/doi/10.5555/3382225.3382372
Tran TPhan T(2018)Towards a Sentiment Analysis Model Based on Semantic Relation AnalysisInternational Journal of Synthetic Emotions10.4018/IJSE.20180701049:2(54-75)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJSE.2018070104
Rakesh VDing WAhuja ARao NSun YReddy CChampin PGandon FMédini LLalmas MIpeirotis P(2018)A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online ReviewsProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186069(1573-1582)Online publication date: 10-Apr-2018
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