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.

References

[1]

Agrawal, R. & Srikant, R. 1994. Fast algorithm for mining association rules. VLDB'94, 1994.

Digital Library

[2]

Boguraev, B., and Kennedy, C. 1997. Salience-Based Content Characterization of Text Documents. In Proc. of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization.

[3]

Bourigault, D. 1995. Lexter: A terminology extraction software for knowledge acquisition from texts. KAW'95.

[4]

Bruce, R., and Wiebe, J. 2000. Recognizing Subjectivity: A Case Study of Manual Tagging. Natural Language Engineering.

Digital Library

[5]

Cardie, C., Wiebe, J., Wilson, T. and Litman, D. 2003. Combining Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering. 2003 AAAI Spring Symposium on New Directions in Question Answering.

[6]

Church, K.W. and Hanks, P. 1990. Word Association Norms, Mutual Information and Lexicography. Computational Linguistics, 16(1):22--29.

Digital Library

[7]

Daille, B. 1996. Study and Implementation of Combined Techniques for Automatic Extraction of Terminology. The Balancing Act: Combining Symbolic and Statistical Approaches to Language. MIT Press, Cambridge

[8]

Das, S. and Chen, M., 2001. Yahoo! for Amazon: Extracting market sentiment from stock message boards. APFA'01.

[9]

Dave, K., Lawrence, S., and Pennock, D., 2003. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. WWW'03.

Digital Library

[10]

DeJong, G. 1982. An Overview of the FRUMP System. Strategies for Natural Language Parsing. 149--176.

[11]

FASTR. http://www.limsi.fr/Individu/jacquemi/FASTR/

[12]

Fellbaum, C. 1998. WordNet: an Electronic Lexical Database, MIT Press.

[13]

Finn, A. and Kushmerick, N. 2003. Learning to Classify Documents according to Genre. IJCAI-03 Workshop on Computational Approaches to Style Analysis and Synthesis.

[14]

Finn, A., Kushmerick, N., and Smyth, B. 2002. Genre Classification and Domain Transfer for Information Filtering. In Proc. of European Colloquium on Information Retrieval Research, pages 353--362.

Digital Library

[15]

Goldstein, J., Kantrowitz, M., Mittal, V., and Carbonell, J. 1999. Summarizing Text Documents: Sentence Selection and Evaluation Metrics. SIGIR'99.

Digital Library

[16]

Hatzivassiloglou, V. and Mckeown, K., 1997. Predicting the Semantic Orientation of Adjectives. In Proc. of 35th ACL/8th EACL.

Digital Library

[17]

Hatzivassiloglou, V. and Wiebe, 2000. J. Effects of Adjective Orientation and Gradability on Sentence Subjectivity. COLING'00.

Digital Library

[18]

Hearst, M, 1992. Direction-based Text Interpretation as an Information Access Refinement. In Paul Jacobs, editor, Text-Based Intelligent Systems. Lawrence Erlbaum Associates.

Digital Library

[19]

Hu, M., and Liu, B. 2004. Mining Opinion Features in Customer Reviews. To appear in AAAI'04, 2004.

Digital Library

[20]

Huettner, A. and Subasic, P., 2000. Fuzzy Typing for Document Management. In ACL'00 Companion Volume: Tutorial Abstracts and Demonstration Notes.

[21]

Jacquemin, C., and Bourigault, D. 2001. Term extraction and automatic indexing. In R. Mitkov, editor, Handbook of Computational Linguistics. Oxford University Press.

[22]

Justeson, J. S., and Katz, S.M. 1995. Technical Terminology: some linguistic properties and an algorithm for identification in text. Natural Language Engineering 1(1):9--27.

[23]

Karlgren, J. and Cutting, D. 1994. Recognizing Text Genres with Simple Metrics using Discriminant Analysis. COLING'94.

Digital Library

[24]

Kessler, B., Nunberg, G., and Schutze, H. 1997. Automatic Detection of Text Genre. In Proc. of 35th ACL/8th EACL.

Digital Library

[25]

Kupiec, J., Pedersen, J., and Chen, F. 1995. A Trainable Document Summarizer. SIGIR'1995

Digital Library

[26]

Liu, B., Hsu, W., Ma, Y. 1998. Integrating Classification and Association Rule Mining. KDD'98, 1998.

[27]

Mani, I., and Bloedorn, E., 1997. Multi-document Summarization by Graph Search and Matching. AAAI'97.

Digital Library

[28]

Manning, C. and Schutze, H. 1999. Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: May 1999.

Digital Library

[29]

Miller, G., Beckwith, R, Fellbaum, C., Gross, D., and Miller, K. 1990. Introduction to WordNet: An on-line lexical database. International Journal of Lexicography (special issue), 3(4):235--312.

[30]

Morinaga, S., Ya Yamanishi, K., Tateishi, K, and Fukushima, T. 2002. Mining Product Reputations on the Web. KDD'02.

Digital Library

[31]

NLProcessor - Text Analysis Toolkit. 2000. http://www.infogistics.com/textanalysis.html

[32]

Paice, C. D. 1990. Constructing Literature Abstracts by Computer: Techniques and Prospects. Information Processing and Management 26:171--186.

Digital Library

[33]

Pang, B., Lee, L., and Vaithyanathan, S., 2002. Thumbs up? Sentiment Classification Using Machine Learning Techniques. In Proc. of EMNLP 2002

Digital Library

[34]

Reimer, U. and Hahn, U. 1997. A Formal Model of Text Summarization based on Condensation Operators of a Terminological Logic. In Proceedings of ACL'97 Workshop on Intelligent, Scalable Text Summarization.

[35]

Sack, W., 1994. On the Computation of Point of View. AAAI'94, Student abstract.

Digital Library

[36]

Salton, G. Singhal, A. Buckley, C. and Mitra, M. 1996. Automatic Text Decomposition using Text Segments and Text Themes. ACM Conference on Hypertext.

Digital Library

[37]

Sparck J. 1993a. Discourse Modeling for Automatic Text Summarizing. Technical Report 290, University of Cambridge Computer Laboratory.

[38]

Sparck J. 1993b. What might be in a summary? Information Retrieval 93: 9--26.

[39]

Tait, J. 1983. Automatic Summarizing of English Texts. Ph.D. Dissertation, University of Cambridge.

[40]

Tetreault, J. 1999. Analysis of Syntax-Based Pronoun Resolution Methods. ACL'99.

Digital Library

[41]

Tong, R., 2001. An Operational System for Detecting and Tracking Opinions in on-line discussion. SIGIR 2001 Workshop on Operational Text Classification.

[42]

Turney, P. 2002. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. ACL'02.

Digital Library

[43]

Wiebe, J. 2000. Learning Subjective Adjectives from Corpora. AAAI'00.

Digital Library

[44]

Wiebe, J., Bruce, R., and O'Hara, T. 1999. Development and Use of a Gold Standard Data Set for Subjectivity Classifications. In Proc. of ACL'99.

Digital Library

Cited By

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
https://doi.org/10.5585/2024.23392
Show More Cited By

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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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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Cited By

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
https://doi.org/10.5585/2024.23392
Patel P(2024)Positive Online Customer Reviews Significantly Boost Sales for Micro-BusinessesIntegrated Journal for Research in Arts and Humanities10.55544/ijrah.4.4.144:4(85-92)Online publication date: 18-Jul-2024
https://doi.org/10.55544/ijrah.4.4.14
Ranjan Praveer SGnankumar P BBaby M(2024)Perceived Thoughts and Tweets: Progression of NEP 2020Asian Journal of Management10.52711/2321-5763.2024.00011(62-68)Online publication date: 21-Mar-2024
https://doi.org/10.52711/2321-5763.2024.00011
Shiva Singh Bhadoria Deepak Gupta Pradeep Yadav (2024)Consumer Sentiment is Extracted from the e-Commerce Website Evaluates Dataset using an Assembly ModelInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-17874(498-504)Online publication date: 28-Apr-2024
https://doi.org/10.48175/IJARSCT-17874
Dr. Anup Bhange Sajid Sheikh Arpit Ninawe (2024)Sentiment Analysis Web AppInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-17565(409-412)Online publication date: 22-Apr-2024
https://doi.org/10.48175/IJARSCT-17565
Lee PSun YKe YLee P(2024)Hotel Rating Prediction System Based on Time FactorsJournal of Organizational and End User Computing10.4018/JOEUC.34212936:1(1-29)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/JOEUC.342129
Demirel SBulur NÇakıcı Z(2024)Utilizing Artificial Intelligence for Text Classification in Communication SciencesDesign and Development of Emerging Chatbot Technology10.4018/979-8-3693-1830-0.ch013(218-235)Online publication date: 15-Mar-2024
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Briciu ACălin AMiholca DMoroz-Dubenco CPetrașcu VDascălu G(2024)Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian ReviewsMathematics10.3390/math1203045612:3(456)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030456
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