research-article

Product feature categorization with multilevel latent semantic association

Authors:

Zhong SuAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1087 - 1096

https://doi.org/10.1145/1645953.1646091

Published: 02 November 2009 Publication History

Abstract

In recent years, the number of freely available online reviews is increasing at a high speed. Aspect-based opinion mining technique has been employed to find out reviewers' opinions toward different product aspects. Such finer-grained opinion mining is valuable for the potential customers to make their purchase decisions. Product-feature extraction and categorization is very important for better mining aspect-oriented opinions. Since people usually use different words to describe the same aspect in the reviews, product-feature extraction and categorization becomes more challenging. Manually product-feature extraction and categorization is tedious and time consuming, and practically infeasible for the massive amount of products. In this paper, we propose an unsupervised product-feature categorization method with multilevel latent semantic association. After extracting product-features from the semi-structured reviews, we construct the first latent semantic association (LaSA) model to group words into a set of concepts according to their virtual context documents. It generates the latent semantic structure for each product-feature. The second LaSA model is constructed to categorize the product-features according to their latent semantic structures and context snippets in the reviews. Experimental results demonstrate that our method achieves better performance compared with the existing approaches. Moreover, the proposed method is language- and domain-independent.

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Cited By

Shin JJoung JLim C(2024)Determining directions of service quality management using online review mining with interpretable machine learningInternational Journal of Hospitality Management10.1016/j.ijhm.2023.103684118(103684)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijhm.2023.103684
Joung JKim H(2023)Interpretable machine learning-based approach for customer segmentation for new product development from online product reviewsInternational Journal of Information Management10.1016/j.ijinfomgt.2023.10264170(102641)Online publication date: Jun-2023
https://doi.org/10.1016/j.ijinfomgt.2023.102641
Xu HXu H(2023)Early Unimodal Sentiment Analysis of Comment Text Based on Traditional Machine LearningMulti-Modal Sentiment Analysis10.1007/978-981-99-5776-7_3(53-134)Online publication date: 27-Nov-2023
https://doi.org/10.1007/978-981-99-5776-7_3
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Index Terms

Product feature categorization with multilevel latent semantic association
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

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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Published: 02 November 2009

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CIKM '09

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CIKM '09: Conference on Information and Knowledge Management

November 2 - 6, 2009

Hong Kong, China

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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https://doi.org/10.1016/j.ijhm.2023.103684
Joung JKim H(2023)Interpretable machine learning-based approach for customer segmentation for new product development from online product reviewsInternational Journal of Information Management10.1016/j.ijinfomgt.2023.10264170(102641)Online publication date: Jun-2023
https://doi.org/10.1016/j.ijinfomgt.2023.102641
Xu HXu H(2023)Early Unimodal Sentiment Analysis of Comment Text Based on Traditional Machine LearningMulti-Modal Sentiment Analysis10.1007/978-981-99-5776-7_3(53-134)Online publication date: 27-Nov-2023
https://doi.org/10.1007/978-981-99-5776-7_3
Qian YXu HWang YLiu YJiang Y(2022)Explore and Interpret the Correlations Among VR Applications2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)10.1109/ISMAR-Adjunct57072.2022.00015(22-26)Online publication date: Oct-2022
https://doi.org/10.1109/ISMAR-Adjunct57072.2022.00015
Joung JKim H(2021)Explainable neural network-based approach to Kano categorisation of product features from online reviewsInternational Journal of Production Research10.1080/00207543.2021.200065660:23(7053-7073)Online publication date: 16-Nov-2021
https://doi.org/10.1080/00207543.2021.2000656
Sivanantham K(2021)Sentiment Analysis on Social Media for Emotional Prediction During COVID‐19 Pandemic Using Efficient Machine Learning ApproachComputational Intelligence and Healthcare Informatics10.1002/9781119818717.ch12(215-233)Online publication date: 25-Oct-2021
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Wang YZhao WWan W(2020)Needs-Based Product Configurator Design for Mass Customization Using Hierarchical Attention NetworkIEEE Transactions on Automation Science and Engineering10.1109/TASE.2019.2957136(1-10)Online publication date: 2020
https://doi.org/10.1109/TASE.2019.2957136
Liu B(2020)Sentiment Analysis10.1017/9781108639286Online publication date: 23-Sep-2020
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Perera RMalepathirana TAbeysinghe YAlbar YThayasivam U(2019)Amalgamation of General and Domain Specific Word Embeddings for Improved Hierarchical Aspect Aggregation2019 IEEE 13th International Conference on Semantic Computing (ICSC)10.1109/ICOSC.2019.8665518(55-62)Online publication date: Jan-2019
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Huang XWu F(2019)A novel topic-based framework for recommending long tail productsComputers & Industrial Engineering10.1016/j.cie.2019.106063(106063)Online publication date: Sep-2019
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