research-article

Public Access

Hidden Topic Sentiment Model

Authors:

Md Mustafizur Rahman,

Hongning WangAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 155 - 165

https://doi.org/10.1145/2872427.2883072

Published: 11 April 2016 Publication History

Abstract

Various topic models have been developed for sentiment analysis tasks. But the simple topic-sentiment mixture assumption prohibits them from finding fine-grained dependency between topical aspects and sentiments. In this paper, we build a Hidden Topic Sentiment Model (HTSM) to explicitly capture topic coherence and sentiment consistency in an opinionated text document to accurately extract latent aspects and corresponding sentiment polarities. In HTSM, 1) topic coherence is achieved by enforcing words in the same sentence to share the same topic assignment and modeling topic transition between successive sentences; 2) sentiment consistency is imposed by constraining topic transitions via tracking sentiment changes; and 3) both topic transition and sentiment transition are guided by a parameterized logistic function based on the linguistic signals directly observable in a document. Extensive experiments on four categories of product reviews from both Amazon and NewEgg validate the effectiveness of the proposed model.

References

[1]

S. Baccianella, A. Esuli, and F. Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In in Proc. of LREC, 2010.

[2]

D. M. Blei and P. J. Moreno. Topic segmentation with an aspect hidden markov model. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 343--348. ACM, 2001.

Digital Library

[3]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003.

Digital Library

[4]

J. Chang, S. Gerrish, C. Wang, J. L. Boyd-Graber, and D. M. Blei. Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems, pages 288--296, 2009.

Digital Library

[5]

A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistical society. Series B (methodological), pages 1--38, 1977.

[6]

Y. Fang, L. Si, N. Somasundaram, and Z. Yu. Mining contrastive opinions on political texts using cross-perspective topic model. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 63--72. ACM, 2012.

Digital Library

[7]

T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. Advances in neural information processing systems, 17:537--544, 2005.

[8]

A. Gruber, Y. Weiss, and M. Rosen-Zvi. Hidden topic markov models. In International Conference on Artificial Intelligence and Statistics, pages 163--170, 2007.

[9]

T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pages 289--296. Morgan Kaufmann Publishers Inc., 1999.

Digital Library

[10]

E. H. Hovy. Automated discourse generation using discourse structure relations. Artificial intelligence, 63(1):341--385, 1993.

Digital Library

[11]

W. Jin, H. H. Ho, and R. K. Srihari. A novel lexicalized hmm-based learning framework for web opinion mining. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 465--472. Citeseer, 2009.

Digital Library

[12]

Y. Jo and A. H. Oh. Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 815--824. ACM, 2011.

Digital Library

[13]

H. Kamp. A theory of truth and semantic representation. Formal methods in the study of language, 1:277--322, 1981.

[14]

D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. Smart stopword list, 2004.

[15]

C. Lin and Y. He. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management, pages 375--384. ACM, 2009.

Digital Library

[16]

J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems, pages 165--172. ACM, 2013.

Digital Library

[17]

J. D. Mcauliffe and D. M. Blei. Supervised topic models. In Advances in neural information processing systems, pages 121--128, 2008.

Digital Library

[18]

Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai. Topic sentiment mixture: modeling facets and opinions in weblogs. In Proceedings of the 16th international conference on World Wide Web, pages 171--180. ACM, 2007.

Digital Library

[19]

D. Mimno and A. McCallum. Topic models conditioned on arbitrary features with dirichlet-multinomial regression. The 24th Conference on Uncertainty in Artificial Intelligence, pages 411--418, 2008.

[20]

K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using em. Machine learning, 39(2--3):103--134, 2000.

Digital Library

[21]

B. Pang and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 115--124. Association for Computational Linguistics, 2005.

Digital Library

[22]

B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1--2):1--135, 2008.

Digital Library

[23]

L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--286, 1989.

[24]

M. Steyvers and T. Griffiths. Probabilistic topic models. Handbook of latent semantic analysis, 427(7):424--440.

[25]

I. Titov and R. T. McDonald. A joint model of text and aspect ratings for sentiment summarization. In ACL, volume 8, pages 308--316. Citeseer, 2008.

[26]

P. D. Turney and M. L. Littman. Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst., 21(4):315--346, Oct. 2003.

Digital Library

[27]

A. J. Viera, J. M. Garrett, et al. Understanding interobserver agreement: the kappa statistic. Fam Med, 37(5):360--363, 2005.

[28]

H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis without aspect keyword supervision. In Proceedings of the 17th ACM SIGKDD Conference, pages 618--626. ACM, 2011.

Digital Library

[29]

H. Wang, D. Zhang, and C. Zhai. Structural topic model for latent topical structure analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 1526--1535. Association for Computational Linguistics, 2011.

Digital Library

[30]

P. Willett. The porter stemming algorithm: then and now. Program, 40(3):219--223, 2006.

[31]

W. X. Zhao, J. Jiang, H. Yan, and X. Li. Jointly modeling aspects and opinions with a maxent-lda hybrid. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 56--65. Association for Computational Linguistics, 2010.

Digital Library

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
Fonseca MDelbianco FMaguitman ASoto A(2023)Assessing causality among topics and sentiments: The case of the G20 discussion on TwitterJournal of Information Science10.1177/01655515231160034Online publication date: 30-Mar-2023
https://doi.org/10.1177/01655515231160034
Li XZou CWu PLi Q(2023)TSSRD: A Topic Sentiment Summarization Framework Based on Reaching DefinitionIEEE Transactions on Affective Computing10.1109/TAFFC.2022.318601514:3(1716-1730)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TAFFC.2022.3186015
Show More Cited By

Index Terms

Hidden Topic Sentiment Model
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

Recommendations

Topic sentiment mixture: modeling facets and opinions in weblogs
WWW '07: Proceedings of the 16th international conference on World Wide Web

In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent ...
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Aspect and sentiment unification model for online review analysis
WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

User-generated reviews on the Web contain sentiments about detailed aspects of products and services. However, most of the reviews are plain text and thus require much effort to obtain information about relevant details. In this paper, we tackle the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

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

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Yahoo
National Science Foundation

Conference

WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

22
Total Citations
View Citations
957
Total Downloads

Downloads (Last 12 months)80
Downloads (Last 6 weeks)20

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

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
Fonseca MDelbianco FMaguitman ASoto A(2023)Assessing causality among topics and sentiments: The case of the G20 discussion on TwitterJournal of Information Science10.1177/01655515231160034Online publication date: 30-Mar-2023
https://doi.org/10.1177/01655515231160034
Li XZou CWu PLi Q(2023)TSSRD: A Topic Sentiment Summarization Framework Based on Reaching DefinitionIEEE Transactions on Affective Computing10.1109/TAFFC.2022.318601514:3(1716-1730)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TAFFC.2022.3186015
Ullah AKhan SNawi N(2022)Review on sentiment analysis for text classification techniques from 2010 to 2021Multimedia Tools and Applications10.1007/s11042-022-14112-382:6(8137-8193)Online publication date: 1-Dec-2022
https://doi.org/10.1007/s11042-022-14112-3
Deepa KBalamurugan DBharath MDineshkumar SVignesh C(2022)Recommendation System Using Deep Learning with Text Mining Algorithm in E-commerce FrameworkComputer Networks and Inventive Communication Technologies10.1007/978-981-19-3035-5_27(345-356)Online publication date: 14-Oct-2022
https://doi.org/10.1007/978-981-19-3035-5_27
Zhang DLauw HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Topic Modeling for Multi-Aspect Listwise ComparisonsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482398(2507-2516)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482398
Sengupta ARoy SRanjan G(2021)LJST: A Semi-supervised Joint Sentiment-Topic Model for Short TextsSN Computer Science10.1007/s42979-021-00649-x2:4Online publication date: 6-May-2021
https://doi.org/10.1007/s42979-021-00649-x
Huang FYuan CBi YLu J(2020)Exploiting Long-Term Dependency for Topic Sentiment AnalysisIEEE Access10.1109/ACCESS.2020.30399638(221963-221974)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3039963
Liang HGaneshbabu UThorne T(2020)A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment EvolutionIEEE Access10.1109/ACCESS.2020.29790128(54164-54174)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2979012
Stern SLivan GSmith R(2020)A network perspective on intermedia agenda-settingApplied Network Science10.1007/s41109-020-00272-45:1Online publication date: 19-Jun-2020
https://doi.org/10.1007/s41109-020-00272-4
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

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

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents