research-article

A holistic lexicon-based approach to opinion mining

Authors:

Philip S. YuAuthors Info & Claims

WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining

Pages 231 - 240

https://doi.org/10.1145/1341531.1341561

Published: 11 February 2008 Publication History

Abstract

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly

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Cited By

Pei GLi HLu YWang YHua SLi T(2024)Affective Computing: Recent Advances, Challenges, and Future TrendsIntelligent Computing10.34133/icomputing.00763Online publication date: 5-Jan-2024
https://doi.org/10.34133/icomputing.0076
Sun XLiu ZLi HYing FTao Y(2024)Chinese text dual attention network for aspect-level sentiment classificationPLOS ONE10.1371/journal.pone.029533119:3(e0295331)Online publication date: 7-Mar-2024
https://doi.org/10.1371/journal.pone.0295331
Gong HZhang S(2024)An aspect sentiment analysis model with Aspect Gated Convolution and Dual-Feature Filtering layersJournal of Big Data10.1186/s40537-024-00969-811:1Online publication date: 9-Aug-2024
https://doi.org/10.1186/s40537-024-00969-8
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Index Terms

A holistic lexicon-based approach to 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

cover image ACM Conferences

WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data Mining

February 2008

270 pages

ISBN:9781595939272

DOI:10.1145/1341531

General Chair:
Marc Najork
Microsoft, USA
,
Program Chairs:
Andrei Broder
Yahoo!, USA
,
Soumen Chakrabarti
IIT Bombay, India

Copyright © 2008 ACM.

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Published: 11 February 2008

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Cited By

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Sun XLiu ZLi HYing FTao Y(2024)Chinese text dual attention network for aspect-level sentiment classificationPLOS ONE10.1371/journal.pone.029533119:3(e0295331)Online publication date: 7-Mar-2024
https://doi.org/10.1371/journal.pone.0295331
Gong HZhang S(2024)An aspect sentiment analysis model with Aspect Gated Convolution and Dual-Feature Filtering layersJournal of Big Data10.1186/s40537-024-00969-811:1Online publication date: 9-Aug-2024
https://doi.org/10.1186/s40537-024-00969-8
Wu LZhang FCheng CSong S(2024)Supervised Contrast Learning Text Classification Model Based on Data Quality AugmentationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/365330023:5(1-12)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3653300
Tang XLiao DShen MZhu LHuang SLi GMan HXu J(2024)Confidence-Aware Sentiment Quantification via Sentiment Perturbation ModelingIEEE Transactions on Affective Computing10.1109/TAFFC.2023.330195615:2(736-750)Online publication date: Apr-2024
https://doi.org/10.1109/TAFFC.2023.3301956
AL-Barrak MAl-Alawi A(2024)Sentiment Analysis on Customer Feedback for Improved Decision Making: A Literature Review2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)10.1109/ICETSIS61505.2024.10459452(207-212)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICETSIS61505.2024.10459452
Afzal SAli Khan HJalil Piran MWeon Lee J(2024)A Comprehensive Survey on Affective Computing: Challenges, Trends, Applications, and Future DirectionsIEEE Access10.1109/ACCESS.2024.342248012(96150-96168)Online publication date: 2024
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Kim JKo Y(2024)SPACE: Senti-Prompt As Classifying Embedding for sentiment analysisPattern Recognition Letters10.1016/j.patrec.2024.02.022Online publication date: Feb-2024
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Chen QHuang JWen WLi QZhang RQin J(2024)CAT: Continual Adapter Tuning for aspect sentiment classificationNeurocomputing10.1016/j.neucom.2024.127423(127423)Online publication date: Feb-2024
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Bu KLiu YJu X(2024)Efficient utilization of pre-trained models: A review of sentiment analysis via prompt learningKnowledge-Based Systems10.1016/j.knosys.2023.111148283(111148)Online publication date: Jan-2024
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