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Article

Mining and summarizing customer reviews

Authors:

Bing LiuAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 168 - 177

https://doi.org/10.1145/1014052.1014073

Published: 22 August 2004 Publication History

Abstract

Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.

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Wu YWan J(2025)A survey of text classification based on pre-trained language modelNeurocomputing10.1016/j.neucom.2024.128921616(128921)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128921
Mao RGe MHan SLi WHe KZhu LCambria E(2025)A survey on pragmatic processing techniquesInformation Fusion10.1016/j.inffus.2024.102712114(102712)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102712
Mamta Ekbal A(2025)Quality achhi hai (is good), satisfied! Towards aspect based sentiment analysis in code-mixed languageComputer Speech & Language10.1016/j.csl.2024.10166889(101668)Online publication date: Jan-2025
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Index Terms

Mining and summarizing customer reviews
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 ACM.

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Publication History

Published: 22 August 2004

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KDD04: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 22 - 25, 2004

WA, Seattle, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1016/j.neucom.2024.128921
Mao RGe MHan SLi WHe KZhu LCambria E(2025)A survey on pragmatic processing techniquesInformation Fusion10.1016/j.inffus.2024.102712114(102712)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102712
Mamta Ekbal A(2025)Quality achhi hai (is good), satisfied! Towards aspect based sentiment analysis in code-mixed languageComputer Speech & Language10.1016/j.csl.2024.10166889(101668)Online publication date: Jan-2025
https://doi.org/10.1016/j.csl.2024.101668
Santhiran RVarathan KChiam Y(2024)Feature extraction from customer reviews using enhanced rulesPeerJ Computer Science10.7717/peerj-cs.182110(e1821)Online publication date: 31-Jan-2024
https://doi.org/10.7717/peerj-cs.1821
Beck DTeixeira MMaróstica JFerasso M(2024)Quality perception of São Paulo transportation services:Revista de Gestão Ambiental e Sustentabilidade10.5585/2024.2339213:1(e23392)Online publication date: 11-Jan-2024
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Bharat A. Shelke Vikas T. Humbe C. Namrata Mahender (2024)A Machine Learning Approach to Enhance Semantic Understanding in Knowledge EngineeringJOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH10.46947/joaasr6320249416:3Online publication date: 30-May-2024
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