research-article

An Association-Based Unified Framework for Mining Features and Opinion Words

Authors:

Christopher C. YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 2

Article No.: 26, Pages 1 - 21

https://doi.org/10.1145/2663359

Published: 31 March 2015 Publication History

Abstract

Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies and propose a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews. We first extract explicit features and opinion words via an association-based bootstrapping method (ABOOT). ABOOT starts with a small list of annotated feature seeds and then iteratively recognizes a large number of domain-specific features and opinion words by discovering the corpus statistics association between each pair of words on a given review domain. Two instances of this ABOOT method are evaluated based on two particular association models, likelihood ratio tests (LRTs) and latent semantic analysis (LSA). Next, we introduce a natural extension to identify implicit features by employing the recognized known semantic correlations between features and opinion words. Experimental results illustrate the benefits of the proposed association-based methods for identifying features and opinion words versus benchmark methods.

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Cited By

Xie JSu RSong J(2022)An analytical study of employee loyalty and corporate culture satisfaction assessment based on sentiment analysisFrontiers in Psychology10.3389/fpsyg.2022.97156913Online publication date: 1-Nov-2022
https://doi.org/10.3389/fpsyg.2022.971569
Soni PRambola R(2022)A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and ScopeIEEE Access10.1109/ACCESS.2022.318320510(63932-63957)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183205
Yan HLi Z(2022)Review of sentiment analysis: An emotional product development viewFrontiers of Engineering Management10.1007/s42524-022-0227-z9:4(592-609)Online publication date: 12-Nov-2022
https://doi.org/10.1007/s42524-022-0227-z
Show More Cited By

Index Terms

An Association-Based Unified Framework for Mining Features and Opinion Words
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 2

Special Section on Visual Understanding with RGB-D Sensors

May 2015

381 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2753829

Editor:
Huan Liu
Arizona State University

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 March 2015

Accepted: 01 August 2014

Revised: 01 June 2014

Received: 01 May 2013

Published in TIST Volume 6, Issue 2

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Singapore Ministry of Education's Academic Research Fund Tier 2

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Cited By

Xie JSu RSong J(2022)An analytical study of employee loyalty and corporate culture satisfaction assessment based on sentiment analysisFrontiers in Psychology10.3389/fpsyg.2022.97156913Online publication date: 1-Nov-2022
https://doi.org/10.3389/fpsyg.2022.971569
Soni PRambola R(2022)A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and ScopeIEEE Access10.1109/ACCESS.2022.318320510(63932-63957)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3183205
Yan HLi Z(2022)Review of sentiment analysis: An emotional product development viewFrontiers of Engineering Management10.1007/s42524-022-0227-z9:4(592-609)Online publication date: 12-Nov-2022
https://doi.org/10.1007/s42524-022-0227-z
Yu LBai X(2021)Implicit Aspect Extraction from Online Clothing Reviews with Fine-tuning BERT AlgorithmJournal of Physics: Conference Series10.1088/1742-6596/1995/1/0120401995:1(012040)Online publication date: 1-Aug-2021
https://doi.org/10.1088/1742-6596/1995/1/012040
Meng YYang NQian ZZhang G(2020)What Makes an Online Review More Helpful: An Interpretation Framework Using XGBoost and SHAP ValuesJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1603002916:3(466-490)Online publication date: 20-Nov-2020
https://doi.org/10.3390/jtaer16030029
Park SLee SOn B(2020)Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product ReviewsApplied Sciences10.3390/app1011383110:11(3831)Online publication date: 31-May-2020
https://doi.org/10.3390/app10113831
Ouyang YZhang HRong WLi XXiong Z(2020)MOOC opinion mining based on attention alignmentInformation Discovery and Delivery10.1108/IDD-01-2020-0012ahead-of-print:ahead-of-printOnline publication date: 22-May-2020
https://doi.org/10.1108/IDD-01-2020-0012
Xu QZhu LDai TGuo LCao S(2019)Non-negative matrix factorization for implicit aspect identificationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01328-911:7(2683-2699)Online publication date: 29-May-2019
https://doi.org/10.1007/s12652-019-01328-9
Nandedkar SPatil J(2018)Co-Extracting Feature and Opinion Pairs From Customer Reviews Using Hybrid Approach2018 3rd International Conference for Convergence in Technology (I2CT)10.1109/I2CT.2018.8529462(1-6)Online publication date: Apr-2018
https://doi.org/10.1109/I2CT.2018.8529462
Shayaa SJaafar NBahri SSulaiman ASeuk Wai PWai Chung YPiprani AAl-Garadi M(2018)Sentiment Analysis of Big Data: Methods, Applications, and Open ChallengesIEEE Access10.1109/ACCESS.2018.28513116(37807-37827)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2851311
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