poster

Walk and learn: a two-stage approach for opinion words and opinion targets co-extraction

Authors:

Jun ZhaoAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 95 - 96

https://doi.org/10.1145/2487788.2487831

Published: 13 May 2013 Publication History

Get Access

Abstract

This paper proposes a novel two-stage method for opinion words and opinion targets co-extraction. In the first stage, a Sentiment Graph Walking algorithm is proposed, which naturally incorporates syntactic patterns in a graph to extract opinion word/target candidates. In the second stage, we adopt a self-Learning strategy to refine the results from the first stage, especially for filtering out noises with high frequency and capturing long-tail terms. Preliminary experimental evaluation shows that considering pattern confidence in the graph is beneficial and our approach achieves promising improvement over three competitive baselines.

References

[1]

M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD '04, pages 168--177.

Digital Library

Google Scholar

[2]

T. Joachims. Transductive inference for text classification using support vector machines. In ICML '99, pages 200--209.

Digital Library

Google Scholar

[3]

G. Qiu, B. Liu, J. Bu, and C. Chen. Expanding domain sentiment lexicon through double propagation. In IJCAI'09, pages 1199--1204.

Digital Library

Google Scholar

[4]

H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis without aspect keyword supervision. In KDD '11, pages 618--626.

Digital Library

Google Scholar

[5]

L. Zhang, B. Liu, S. H. Lim, and E. O'Brien-Strain. Extracting and ranking product features in opinion documents. In COLING '10, pages 1462--1470.

Digital Library

Google Scholar

Cited By

View all

Yadavilli VSeshadri K(2022)A Survey on Aspect Extraction Approaches for Sentiment AnalysisResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch068(1314-1337)Online publication date: 10-Jun-2022
https://doi.org/10.4018/978-1-6684-6303-1.ch068
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: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10462-021-10134-9
Yadavilli VSeshadri K(2021)A Survey on Aspect Extraction Approaches for Sentiment AnalysisData Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance10.4018/978-1-7998-7371-6.ch003(42-65)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7371-6.ch003
Show More Cited By

Index Terms

Walk and learn: a two-stage approach for opinion words and opinion targets co-extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach

This article describes how e-commerce has become so vast that almost every product and service can be purchased online, to be delivered at our doorsteps. This has led to a striking increase in the number of online customers. In an attempt to make the ...
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 ...
Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments
Abstract
User comments are valuable resources for software improvement; however, owing to excessive volume, informative comments need to be selectively analyzed. We attempt to address this problem by sentiment analysis and expect sentiment can be a useful ...

Comments

Information & Contributors

Information

Published In

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

Check for updates

Author Tags

Qualifiers

Poster

Conference

WWW '13

Sponsor:

NICBR
CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

Acceptance Rates

WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

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

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
180
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yadavilli VSeshadri K(2022)A Survey on Aspect Extraction Approaches for Sentiment AnalysisResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch068(1314-1337)Online publication date: 10-Jun-2022
https://doi.org/10.4018/978-1-6684-6303-1.ch068
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: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10462-021-10134-9
Yadavilli VSeshadri K(2021)A Survey on Aspect Extraction Approaches for Sentiment AnalysisData Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance10.4018/978-1-7998-7371-6.ch003(42-65)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7371-6.ch003
Mehanna YMahmuddin M(2021)A Semantic Conceptualization Using Tagged Bag-of-Concepts for Sentiment AnalysisIEEE Access10.1109/ACCESS.2021.31072379(118736-118756)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3107237
Ahmad SBakar AYaakub M(2019)A review of feature selection techniques in sentiment analysisIntelligent Data Analysis10.3233/IDA-17376323:1(159-189)Online publication date: 20-Feb-2019
https://doi.org/10.3233/IDA-173763
Rana TCheah Y(2017)A two-fold rule-based model for aspect extractionExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.07.04789:C(273-285)Online publication date: 15-Dec-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.07.047
Pu Zhang Junxia Wang Yinghao Wang Yong Wang (2016)A statistical approach to opinion target extraction using domain relevance2016 2nd IEEE International Conference on Computer and Communications (ICCC)10.1109/CompComm.2016.7924708(273-277)Online publication date: Oct-2016
https://doi.org/10.1109/CompComm.2016.7924708
Rana TCheah Y(2016)Aspect extraction in sentiment analysisArtificial Intelligence Review10.1007/s10462-016-9472-z46:4(459-483)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s10462-016-9472-z
Xu YLin TLam WZhou ZCheng HSo ALi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Latent Aspect Mining via Exploring Sparsity and Intrinsic InformationProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2662062(879-888)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2662062

View Options

Get Access

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

Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach

Joint sentiment/topic model for sentiment analysis

Does sentiment help requirement engineering: exploring sentiments in user comments to discover informative comments