Article

The utility of linguistic rules in opinion mining

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SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 811 - 812

https://doi.org/10.1145/1277741.1277921

Published: 23 July 2007 Publication History

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Abstract

Online product reviews are one of the important opinion sources on the Web. This paper studies the problem of determining the semantic orientations (positive or negative) of opinions expressed on product features in reviews. Most existing approaches use a set of opinion words for the purpose. However, the semantic orientations of many words are context dependent. In this paper, we propose to use some linguistic rules to deal with the problem together with a new opinion aggregation function. Extensive experiments show that these rules and the function are highly effective. A system, called Opinion Observer, has also been built.

References

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V. Hatzivassiloglou, and K. McKeown, Predicting the Semantic Orientation of Adjectives. ACL-97, 1997.

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M. Hu and B. Liu. Mining and Summarizing Customer Reviews. KDD-04, 2004.

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[3]

H. Kanayama and T. Nasukawa. Fully Automatic Lexicon Expansion for Domain-Oriented Sentiment Analysis. EMNLP-06, 2006.

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B. Liu. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, 2007.

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A-M. Popescu and O. Etzioni. Extracting Product Features and Opinions from Reviews. EMNLP--05, 2005.

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Cited By

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Wang YYang NMiao DChen Q(2024)Aspect-Guided Multi-Graph Convolutional Networks for Aspect-based Sentiment AnalysisData Intelligence10.3724/2096-7004.di.2024.00526:3(771-791)Online publication date: 6-Sep-2024
https://doi.org/10.3724/2096-7004.di.2024.0052
You LPeng JJin HClaramunt CZeng HZhang Z(2024)DRGAT: Dual-Relational Graph Attention Networks for Aspect-based Sentiment ClassificationInformation Sciences10.1016/j.ins.2024.120531(120531)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120531
Ling TChen LLiao CHuang SYu ZLiu Y(2023)Improving Aspect Sentiment Classification via Retrieving from Training Data2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC58517.2023.10317451(490-497)Online publication date: 31-Oct-2023
https://doi.org/10.1109/APSIPAASC58517.2023.10317451
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Index Terms

The utility of linguistic rules in opinion mining
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

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Published In

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2007

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SIGIR07

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Wang YYang NMiao DChen Q(2024)Aspect-Guided Multi-Graph Convolutional Networks for Aspect-based Sentiment AnalysisData Intelligence10.3724/2096-7004.di.2024.00526:3(771-791)Online publication date: 6-Sep-2024
https://doi.org/10.3724/2096-7004.di.2024.0052
You LPeng JJin HClaramunt CZeng HZhang Z(2024)DRGAT: Dual-Relational Graph Attention Networks for Aspect-based Sentiment ClassificationInformation Sciences10.1016/j.ins.2024.120531(120531)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120531
Ling TChen LLiao CHuang SYu ZLiu Y(2023)Improving Aspect Sentiment Classification via Retrieving from Training Data2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC58517.2023.10317451(490-497)Online publication date: 31-Oct-2023
https://doi.org/10.1109/APSIPAASC58517.2023.10317451
Wu ZCao GMo W(2023)Multi-Tasking for Aspect-Based Sentiment Analysis via Constructing Auxiliary Self-Supervision ACOP TaskIEEE Access10.1109/ACCESS.2023.327632011(82924-82932)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3276320
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Behdenna SBarigou FBelalem G(2022)Towards Semantic Aspect-Based Sentiment Analysis for Arabic ReviewsResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch077(1480-1493)Online publication date: 10-Jun-2022
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BARAN ERGÜL DVAROL MALKOÇOĞLU AACUN ÖZGÜNLER S(2022)Mimari Tasarım Karar Verme Süreçlerinde Yapay Zekâ Tabanlı Bulanık Mantık Sistemerinin DeğerlendirilmesiUse of Artificial Intelligence Based Fuzzy Logic Systems in Architectural Design Decision Making ProcessesMimarlık Bilimleri ve Uygulamaları Dergisi (MBUD)10.30785/mbud.11179107:2(878-899)Online publication date: 30-Dec-2022
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Wu OYang TLi MLi M(2022)Two-Level LSTM for Sentiment Analysis With Lexicon Embedding and Polar FlippingIEEE Transactions on Cybernetics10.1109/TCYB.2020.301737852:5(3867-3879)Online publication date: May-2022
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