A Transaction Frequency Based Trust for E-Commerce

Dong, Hui; Xu, Sean Xin

doi:10.32604/cmc.2023.033798

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…We hope that the results of this research will help the development of sentiment analysis in the context of e-commerce and in turn help e-commerce companies to improve their customer service and experience. Finally, Huang and Xu (2023) developed a transaction frequency-based method for trust in e-commerce, illustrating the diversity of Dempster-Shafer uses in this domain [16][17][18] [19]. In the context of this research, Dempster-Shafer proves to be a powerful framework for addressing uncertainty and complexity in consumer sentiment analysis in a rapidly evolving e-commerce environment.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Customers in E-Commerce Using Dempster-Shafer Method

Nazaruddin,

Caroline,

Andrijanni

et al. 2023

ijsecs

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This research explores the analysis of consumer sentiment in the context of e-commerce by applying the sophisticated Dempster-Shafer method. We started with the collection of more than 20,000 consumer reviews from various leading e-commerce platforms and continued with a detailed data pre-processing stage to obtain a clean and structured dataset. Next, we leverage the Dempster-Shafer method to represent and combine information from multiple sources, addressing uncertainty in diverse consumer opinions. The results of the sentiment analysis show that the Dempster-Shafer method achieves an accuracy of around 85%, with good evaluation metrics. Additionally, this research provides insight into the factors that influence consumers' views of products or services in the growing e-commerce context. The literature review also reveals the potential application of the Dempster-Shafer method in other aspects of e-commerce business, such as risk management and consumer trust. This research highlights the contribution of the Dempster-Shafer method in addressing uncertainty and complexity in consumer sentiment analysis, yielding a deep understanding of consumer perceptions, and enabling more accurate decision making in a dynamic e-commerce context. This research also provides a foundation for further development in consumer sentiment analysis and the application of the Dempster-Shafer method in e-commerce.

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Section: Discussionmentioning

confidence: 99%