Title: A method for mining comment text data on e-commerce platforms for enterprise digital transformation

Authors: Yang Wang

Addresses: Business School, Huanggang Normal University, Huanggang, 438000, China

Abstract: In order to solve the problems of poor data mining performance, high RMSE value, and low stability variance in traditional e-commerce platform comment text data mining methods, a new method for mining comment text data on e-commerce platforms for enterprise digital transformation is proposed. Firstly, LDA is used for dimensionality reduction of comment text data; Secondly, the information gain method is applied to screen the key features of the comment text data based on the dimensionality reduction results; Finally, based on the selected key features, the K-means algorithm is used to cluster and mine the comment text data. The experimental results show that this method can effectively identify and classify different types of comment data, and obtain more accurate mining results. After multiple iterations, its RMSE index remained stable at around 0.2, and the highest stability variance reached 98.3, indicating that its data mining results were more accurate and stable.

Keywords: enterprise digital transformation; e-commerce platform; text data mining; LDA; key features; k-means algorithm.

DOI: 10.1504/IJBIDM.2025.149065

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.139 - 152

Received: 08 Nov 2024
Accepted: 16 Jan 2025
Published online: 13 Oct 2025 *

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