Abstract
Grounded Theory Analysis of online shopping reviews can improve the quality and accuracy of review data analysis, providing valuable information and suggestions for relevant businesses and consumers. This article focuses on analyzing mobile phone reviews on JD.com as coding objects, effectively employing machine learning algorithms such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and text clustering in natural language processing to extract thematic keywords from online shopping review texts and perform axial coding. Subsequently, DBSCAN clustering algorithm is applied to categorize the main categories of design, service, and production under the perspective of businesses and consumers as consumption attraction, while categorizing price and brand under consumption intention, forming selective coding. By applying Grounded Theory to the analysis of online shopping reviews, this study accurately identifies consumer needs and feedback, with the aim of helping businesses improve product and service quality, enhance customer satisfaction and loyalty, and engage in personalized marketing and customized services.
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