Abstract
With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.
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