Open AccessText mining-based analysis of online comments for skincare e-commerce
Author(s) -
Hao Wen,
Yiping Wu,
Shangze Li,
Ying Wu,
Zehai Zhou,
Y. Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012008
Subject(s) - cluster analysis , product (mathematics) , e commerce , computer science , value (mathematics) , marketing , business , data science , world wide web , artificial intelligence , machine learning , geometry , mathematics
This article uses online review data of skincare products on e-commerce platforms to summarize the consumer demand characteristics through text clustering analysis and offer common marketing proposals for skincare merchants. From the role of review text clustering analysis, this paper derives two dimensions from e-commerce platforms and skin care product categories, and through feature extraction and lexical item clustering analysis of consumer online review information on different platforms, the focus of attention and characteristic tendencies of consumers on skin care products on different platforms are mined. In turn, the review information can be mined and analyzed to obtain information with business value, and relevant measures can be taken to improve the platform’s services, promote business growth, enhance customer satisfaction, etc. First, develop a reasonable marketing strategy. Second, strengthen product branding.