research-article

Opinion integration through semi-supervised topic modeling

Authors:

Chengxiang ZhaiAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 121 - 130

https://doi.org/10.1145/1367497.1367514

Published: 21 April 2008 Publication History

Abstract

Web 2.0 technology has enabled more and more people to freely express their opinions on the Web, making the Web an extremely valuable source for mining user opinions about all kinds of topics. In this paper we study how to automatically integrate opinions expressed in a well-written expert review with lots of opinions scattering in various sources such as blogspaces and forums. We formally define this new integration problem and propose to use semi-supervised topic models to solve the problem in a principled way. Experiments on integrating opinions about two quite different topics (a product and a political figure) show that the proposed method is effective for both topics and can generate useful aligned integrated opinion summaries. The proposed method is quite general. It can be used to integrate a well written review with opinions in an arbitrary text collection about any topic to potentially support many interesting applications in multiple domains.

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Cited By

Chen MMao YHua QWu J(2023)Aspect Extraction from E-Commerce Reviews Based on Word-Word Relationship Classification2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT60137.2023.10528431(226-231)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ACAIT60137.2023.10528431
Ma JWang LZhang YYuan WGuo W(2023)An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to localExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118695212:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118695
Aggarwal CAggarwal C(2022)Opinion Mining and Sentiment AnalysisMachine Learning for Text10.1007/978-3-030-96623-2_15(491-514)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-96623-2_15
Show More Cited By

Index Terms

Opinion integration through semi-supervised topic modeling
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Chen MMao YHua QWu J(2023)Aspect Extraction from E-Commerce Reviews Based on Word-Word Relationship Classification2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT60137.2023.10528431(226-231)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ACAIT60137.2023.10528431
Ma JWang LZhang YYuan WGuo W(2023)An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to localExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118695212:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118695
Aggarwal CAggarwal C(2022)Opinion Mining and Sentiment AnalysisMachine Learning for Text10.1007/978-3-030-96623-2_15(491-514)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-96623-2_15
Cheng QZhu YSong JZeng HWang SSun KZhang J(2021)Bert-Based Latent Semantic Analysis (Bert-LSA): A Case Study on Geospatial Data Technology and Application Trend AnalysisApplied Sciences10.3390/app11241189711:24(11897)Online publication date: 14-Dec-2021
https://doi.org/10.3390/app112411897
Huang JTao NChen HDeng QWang WWang J(2021) Semi-supervised Text Classification Based On Graph Attention Neural Networks * 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD)10.1109/ICAIBD51990.2021.9459003(325-330)Online publication date: 28-May-2021
https://doi.org/10.1109/ICAIBD51990.2021.9459003
Zhou JZhao JHuang JHu QHe L(2021)MASAD: A large-scale dataset for multimodal aspect-based sentiment analysisNeurocomputing10.1016/j.neucom.2021.05.040455(47-58)Online publication date: Sep-2021
https://doi.org/10.1016/j.neucom.2021.05.040
Subhashini LLi YZhang JAtukorale AWu Y(2021)Mining and classifying customer reviews: a surveyArtificial Intelligence Review10.1007/s10462-021-09955-554:8(6343-6389)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10462-021-09955-5
Gera TSingh JThakur DFaruki P(2021)A Semi-automated Approach for Identification of Trends in Android Ransomware LiteratureMachine Learning for Networking10.1007/978-3-030-70866-5_18(265-283)Online publication date: 3-Mar-2021
https://doi.org/10.1007/978-3-030-70866-5_18
Zhang YMeng YHuang JXu FWang XHan JHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Minimally Supervised Categorization of Text with MetadataProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401168(1231-1240)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401168
Subhashini LLi YZhang JAtukorale A(2020)Integration of Fuzzy and Deep Learning in Three-Way Decisions2020 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW51313.2020.00019(71-78)Online publication date: Nov-2020
https://doi.org/10.1109/ICDMW51313.2020.00019
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