Version 1
: Received: 25 September 2024 / Approved: 26 September 2024 / Online: 26 September 2024 (09:56:51 CEST)
How to cite:
Sun, Z.; Mao, Y.; Zong, Q.; Wu, G. Exploring the Features and Trends of Industrial Products E-Commerce in China by Using Text Mining Approaches. Preprints2024, 2024092080. https://doi.org/10.20944/preprints202409.2080.v1
Sun, Z.; Mao, Y.; Zong, Q.; Wu, G. Exploring the Features and Trends of Industrial Products E-Commerce in China by Using Text Mining Approaches. Preprints 2024, 2024092080. https://doi.org/10.20944/preprints202409.2080.v1
Sun, Z.; Mao, Y.; Zong, Q.; Wu, G. Exploring the Features and Trends of Industrial Products E-Commerce in China by Using Text Mining Approaches. Preprints2024, 2024092080. https://doi.org/10.20944/preprints202409.2080.v1
APA Style
Sun, Z., Mao, Y., Zong, Q., & Wu, G. (2024). Exploring the Features and Trends of Industrial Products E-Commerce in China by Using Text Mining Approaches. Preprints. https://doi.org/10.20944/preprints202409.2080.v1
Chicago/Turabian Style
Sun, Z., Qi Zong and Gongxing Wu. 2024 "Exploring the Features and Trends of Industrial Products E-Commerce in China by Using Text Mining Approaches" Preprints. https://doi.org/10.20944/preprints202409.2080.v1
Abstract
Industrial products e-commerce refers to the specific application of the e-commerce concept in the field of industrial products transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industry literature such as policies, reports and standards related to industrial products e-commerce contains abundant and crucial information. Through systematical analysis of the literature information, it is beneficial to explore and grasp the development characteristics and trends of the industrial products e-commerce. To study the characteristics and development status of industrial products e-commerce in China, literature comprising 18 policy documents, 10 industrial reports and 5 standards is deeply analyzed by employing text mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial products commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the results of word frequency statistics are visually displayed. Subsequently, the feature set is obtained by combining the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in word2vec, and the feature words are transformed into word vectors in the multi-dimensional space. The k-means algorithm is used to cluster the feature words into groups. Moreover, the latent dirichlet allocation (LDA) method is also utilized to further group and discover the features. The research results based on text mining provide supportive decisions for uncovering the development characteristics and trends of industrial products e-commerce in China.
Keywords
industrial products e-commerce; e-commerce platform; text mining; policy research; standard analysis
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
