Comparative analysis of multi-platform e-commerce online reviews based LDA and sentiment dictionary
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 17 December 2024
Abstract
Purpose
This study aims to investigate the differences in consumer reviews across multiple e-commerce platforms to better assist consumers in making informed decisions. By examining the specific content of these differentiated reviews, the study seeks to provide insights that can enhance e-commerce services and improve consumer satisfaction.
Design/methodology/approach
The research utilizes the latent Dirichlet allocation (LDA) method for text analysis to identify the varying concerns of consumers across different e-commerce platforms for the same product. Additionally, the study expands the sentiment dictionary to address polysemy issues, allowing for a more precise capture of sentiment differences among consumers. A non-parametric test is employed to compare reviews across multiple platforms, providing a comprehensive analysis of review disparities.
Findings
The findings reveal that consumer concerns and sentiments vary significantly across different e-commerce platforms, even for the same product. The combination of text analysis and non-parametric testing highlights the objectivity of the research, offering valuable evidence and recommendations for improving e-commerce services and enhancing the shopping experience.
Originality/value
This study is original in its approach to combining text analysis with non-parametric testing to examine multi-platform review differences. The research not only contributes to the understanding of consumer behavior in the context of e-commerce but also provides practical suggestions for platforms and consumers, aiming to optimize service quality and consumer satisfaction.
Keywords
Acknowledgements
This work was supported by the General Project of Philosophy and Social Science Research in Jiangsu Universities [Nos. 2022SJYB0185], National Natural Science Foundation of China [Nos. 72201134], the Natural Science Foundation of Jiangsu Province [Nos. BK20210632], Research Institute for Risk Governance and Emergency Decision-Making, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China. and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, China. The authors declare no conflicts of interest.
Citation
Xie, W., Zhao, W. and Xu, Z. (2024), "Comparative analysis of multi-platform e-commerce online reviews based LDA and sentiment dictionary", International Journal of Intelligent Computing and Cybernetics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJICC-08-2024-0384
Publisher
:Emerald Publishing Limited
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