poster

Multi-aspect opinion polling from textual reviews

Authors:

Jingbo Zhu,

Huizhen Wang,

Benjamin K. Tsou,

Muhua ZhuAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1799 - 1802

https://doi.org/10.1145/1645953.1646233

Published: 02 November 2009 Publication History

Get Access

Abstract

This paper presents an unsupervised approach to aspect-based opinion polling from raw textual reviews without explicit ratings. The key contribution of this paper is three-fold. First, a multi-aspect bootstrapping algorithm is proposed to learn from unlabeled data aspect-related terms of each aspect to be used for aspect identification. Second, an unsupervised segmentation model is proposed to address the challenge of identifying multiple single-aspect units in a multi-aspect sentence. Finally, an aspect-based opinion polling algorithm is presented. Experiments on real Chinese restaurant reviews show that our opinion polling method can achieve 75.5% precision performance.

References

[1]

Pang B. and Lee L. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, Vol. 2, Nos. 1-2(2008) 1--135

Digital Library

Google Scholar

[2]

Hu M. and Liu B. 2004. Mining and summarizing customer reviews. In Proceedings of the 2004 ACM SIGKDD international conference on knowledge discovery and data mining, pp. 168--177.

Digital Library

Google Scholar

[3]

Pang, B. Lee L., and Vaithyanathan S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In proc. of EMNLP02

Digital Library

Google Scholar

[4]

Riloff E., Patwardhan S., and Wiebe J. 2006. Feature subsumption for opinion analysis. In Proceedings of EMNLP06, pp. 440--448

Digital Library

Google Scholar

[5]

Frantzi K., Ananiadou S., Mima H. 2000. Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal on Digital Libraries, p. 115--130

Crossref

Google Scholar

[6]

Riloff E. and Jones R. 1999. Learning dictionaries for information extraction by multi-Level bootstrapping, Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99)

Digital Library

Google Scholar

[7]

Wan X. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of EMNLP08, pp. 553--561

Digital Library

Google Scholar

[8]

Gamon M., Aue A., Corston-Oliver S., and Ringger E. 2005. Pulse: mining customer opinions from free text. In Proceedings of the 6th international symposium on intelligent data analysis, pp. 121--132

Digital Library

Google Scholar

[9]

Zhuang L., Jing F. and Zhu X. Y. 2006. Movie review mining and summarization. In Proceedings of the 15th ACM international conference on information and knowledge management, pp. 43--50

Digital Library

Google Scholar

[10]

Riloff, E., Wiebe J., and Wilson T. 2003. Learning subjective nouns using extraction pattern Bootstrapping. In Prof. of CoNLL-03

Digital Library

Google Scholar

[11]

Zagibalov T., and Carroll J. 2008. Unsupervised Classification of Sentiment and Objectivity in Chinese Text. In Proceedings of Proceedings of the Third International Joint Conference on Natural Language Processing, pp. 304--311

Google Scholar

[12]

Pang B. and Lee L. 2005. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the ACL05, pp. 115--124

Digital Library

Google Scholar

[13]

Snyder B. and Barzilay R. 2007. Multiple aspect ranking using the good grief algorithm. In Proceedings of NAACL/HLT-2007, pp. 300--307

Google Scholar

Cited By

View all

Deng HLi YJu SLiu M(2023)Combines Contrastive Learning and Primary Capsule Encoder for Target Sentiment ClassificationWeb Information Systems and Applications10.1007/978-981-99-6222-8_24(284-296)Online publication date: 9-Sep-2023
https://doi.org/10.1007/978-981-99-6222-8_24
POLATGİL MTUNA MKAYNAR O(2022)Kelime Temsil Yöntemlerinin Hedef Tabanlı Duygu Analizine EtkisiThe Effect of Word Representation Methods on Aspect-Based Sentiment AnalysisBilişim Teknolojileri Dergisi10.17671/gazibtd.111490115:4(443-452)Online publication date: 31-Oct-2022
https://doi.org/10.17671/gazibtd.1114901
Brauwers GFrasincar F(2022)A Survey on Aspect-Based Sentiment ClassificationACM Computing Surveys10.1145/350304455:4(1-37)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1145/3503044
Show More Cited By

Index Terms

Multi-aspect opinion polling from textual reviews
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Aspect-Based Opinion Polling from Customer Reviews

Opinion polling has been traditionally done via customer satisfaction studies in which questions are carefully designed to gather customer opinions about target products or services. This paper studies aspect-based opinion polling from unlabeled free-...
Aspect-based extractive summarization of online reviews
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

In this paper, we study the aspect-based extractive summarization based on the observations that a good summary should present representative opinions on user concerned sub-aspects within limited words. According to these observations, we argue that, ...
Latent aspect rating analysis without aspect keyword supervision
KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

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 ...

Comments

Information & Contributors

Information

Published In

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM '09

Sponsor:

CIKM '09: Conference on Information and Knowledge Management

November 2 - 6, 2009

Hong Kong, China

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

46
Total Citations
View Citations
946
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Deng HLi YJu SLiu M(2023)Combines Contrastive Learning and Primary Capsule Encoder for Target Sentiment ClassificationWeb Information Systems and Applications10.1007/978-981-99-6222-8_24(284-296)Online publication date: 9-Sep-2023
https://doi.org/10.1007/978-981-99-6222-8_24
POLATGİL MTUNA MKAYNAR O(2022)Kelime Temsil Yöntemlerinin Hedef Tabanlı Duygu Analizine EtkisiThe Effect of Word Representation Methods on Aspect-Based Sentiment AnalysisBilişim Teknolojileri Dergisi10.17671/gazibtd.111490115:4(443-452)Online publication date: 31-Oct-2022
https://doi.org/10.17671/gazibtd.1114901
Brauwers GFrasincar F(2022)A Survey on Aspect-Based Sentiment ClassificationACM Computing Surveys10.1145/350304455:4(1-37)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1145/3503044
Xiao LXue YWang HHu XGu DZhu Y(2022)Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networksNeurocomputing10.1016/j.neucom.2021.10.091471:C(48-59)Online publication date: 30-Jan-2022
https://dl.acm.org/doi/10.1016/j.neucom.2021.10.091
Singh AJenamani MThakkar JRana N(2022)Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approachJournal of Business Research10.1016/j.jbusres.2021.08.060138(52-64)Online publication date: Jan-2022
https://doi.org/10.1016/j.jbusres.2021.08.060
Bensoltane RZaki T(2022)Aspect-based sentiment analysis: an overview in the use of Arabic languageArtificial Intelligence Review10.1007/s10462-022-10215-356:3(2325-2363)Online publication date: 5-Jul-2022
https://doi.org/10.1007/s10462-022-10215-3
D’Aniello GGaeta MLa Rocca I(2022)KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysisArtificial Intelligence Review10.1007/s10462-021-10134-955:7(5543-5574)Online publication date: 9-Jan-2022
https://doi.org/10.1007/s10462-021-10134-9
Arumugam S(2022)Development of argument based opinion mining model with sentimental data analysis from twitter contentConcurrency and Computation: Practice and Experience10.1002/cpe.695634:15Online publication date: 11-Apr-2022
https://doi.org/10.1002/cpe.6956
Gupta AGandhi AAgarwal SChokshi SK S(2021)Sentiment Analysis-Enhancements and ApplicationsInternational Journal of Recent Technology and Engineering (IJRTE)10.35940/ijrte.A1409.111042110:4(56-70)Online publication date: 30-Nov-2021
https://doi.org/10.35940/ijrte.A1409.1110421
Shaalan YZhang XChan JSalehi M(2021)Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patternsData Mining and Knowledge Discovery10.1007/s10618-020-00725-5Online publication date: 2-Jan-2021
https://doi.org/10.1007/s10618-020-00725-5
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Aspect-Based Opinion Polling from Customer Reviews

Aspect-based extractive summarization of online reviews

Latent aspect rating analysis without aspect keyword supervision

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Aspect-Based Opinion Polling from Customer Reviews

Aspect-based extractive summarization of online reviews

Latent aspect rating analysis without aspect keyword supervision

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations