Journal article
A Classification-Based Product Selection Method Based on Online Reviews on Multifaceted Attributes
IEEE transactions on computational social systems, pp 1-14
07 Nov 2024
Abstract
While the development of e-commerce brings convenience to consumers, a large quantity of products and information increase the difficulty of making purchase decisions. This study constructs a classification-based product selection method driven by online reviews to assist consumers in making purchase decisions. First, the multifaceted attribute evaluations of products are extracted from textual reviews that contain more abundant and useful information than those provided by vendors. The evaluations are modeled by probabilistic linguistic term sets such that sentiment words in texts are described at different frequencies. Then, a classification-based product selection method is developed to rank products considering multifaceted attributes in which alternative products are divided into the acceptance class, rejection class, and uncertainty class through a classification strategy. Each class of products is compared based on the performance scores calculated by a probabilistic linguistic aggregation operator. A case study of selecting laptops based on real data from Amazon.com is given to illustrate the method. Comparative analysis with existing ranking methods shows the advantages of the proposed method in matching consumers' risk aversion behavior and preserving uncertain information.
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1 citations in Scopus
Details
- Title
- A Classification-Based Product Selection Method Based on Online Reviews on Multifaceted Attributes
- Creators
- Xingli Wu - Sichuan UniversityHuchang Liao - Sichuan UniversityBenjamin Lev - Drexel UniversityWeiping Ding - Nantong University
- Publication Details
- IEEE transactions on computational social systems, pp 1-14
- Publisher
- IEEE; PISCATAWAY
- Number of pages
- 14
- Grant note
- National Natural Science Foundation of China: 72301186, 72371173, 72171158 Sichuan Science and Technology Program: 2024NSFSC1065
The work was supported in part by the National Natural Science Foundation of China under Grant 72301186, Grant 72371173, and Grant 72171158; and in part by the Sichuan Science and Technology Program under Grant 2024NSFSC1065.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001351449400001
- Scopus ID
- 2-s2.0-85209748354
- Other Identifier
- 991021959778804721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Computer Science, Information Systems