A Customer Demand Mining Algorithm Based on Online Comments and Machine Learning
DOI:
https://doi.org/10.23055/ijietap.2025.32.1.9161Abstract
In the current market environment, the phenomenon of product homogenization is severe. If enterprises cannot deeply understand customer needs and provide differentiated products or services, it is difficult to stand out in the competition. In order to effectively improve overall customer satisfaction and enhance the market competitiveness of enterprises, a customer demand mining algorithm based on online comments and machine learning is proposed. Collect customer demand information data through online comments and process the collected data with redundant information to improve the efficiency and accuracy of demand mining. On this basis, customer demand attribute features have been further extracted, and a customer demand clustering mining model has been constructed using a self-organizing mapping neural network. By training the model, the final clustering mining results can be obtained, thus achieving precise mining of customer needs. This study clearly addresses a key issue in the current field of consumer demand mining: how to efficiently and accurately identify and utilize consumer demand information in online comments. By constructing a clustering mining model based on the Self-Organizing Maps (SOM) neural network, this study fills the literature gap in this field and provides more accurate and practical consumer demand analysis methods for enterprises. The experimental results show that, compared with the three comparison methods, the proposed method has a 98% feasibility of customer demand mining and 92% customer satisfaction. It shows that the proposed method has high feasibility and customer satisfaction for customer demand mining and has a better overall customer demand mining effect. This provides strong support for improving overall customer satisfaction and corporate competitiveness.
Downloads
Published
How to Cite
Issue
Section
License
The Author(s) must formally transfer each article's copyright before publication in the INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING. Such transfer enables the Journal to defend itself against plagiarism and other forms of copyright infringement. Your cooperation is appreciated.
You agree that the copyright of your article to be published in the INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING - THEORY, APPLICATIONS, AND PRACTICE is hereby transferred, throughout the World and for the full term and all extensions and renewals thereof, to INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING - THEORY, APPLICATIONS, AND PRACTICE.
The Author(s) reserve(s): (a) the trademark rights and patent rights, if any, and (b) the right to use all or part of the information contained in this article in future, non-commercial works of the Author's own, or, if the article is a "work-for-hire" and made within the scope of the Author's employment, the employer may use all or part of the information contained in this article for intra-company use, provided the usual acknowledgments are given regarding copyright notice and reference to the original publication.
The Author(s) warrant(s) that the article is Author's original work and has not been published before. If excerpts from copyrighted works are included, the Author will obtain written permission from the copyright owners and credit the article's sources.
The author also warrants that the article contains no libelous or unlawful statements and does not infringe on the rights of others. If the article was prepared jointly with other Author(s), the Author agrees to inform the co-Author(s) of the terms of the copyright transfer and to sign on their behalf; or in the case of a "work-for-hire," the employer or an authorized representative of the employer.
The journal does not provide the author copy of the final paper when it is published. The author(s) can make(s) a subscription to INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING - THEORY, APPLICATIONS, AND PRACTICE if they want to get the final paper that has already been published.
The journal is registered with the Library of Congress (ISSN # 1943-670X). All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the journal.
The author reserves patent and trademark rights and the right to use all or part of the information contained in the article in future non-commercial works.
