LDA-BASED COSMETIC SATISFACTION FACTORS MINING
Chen Wang, Qigan Shao · 2022
With the rapid development of Internet technology, the convenience of the information age makes it possible for consumers to buy satisfactory products without leaving home. Compared with offline purchase, online purchases of cosmetic products is more convenient and has more kinds of products for users to choose from, and the price and shopping cost of the products are also lower, but the quality, branding, and after-sales service of online products are often criticized by people, and these problems seriously affect user satisfaction. In this paper, we use a text mining method to crawl online reviews of lipsticks as a data source using python language to explore the factors affecting consumer satisfaction, taking a typical experience-based product, lipstick, as an example. Then the LDA topic model is used to cluster the processed text, and the factors affecting consumer satisfaction are summarized and summarized according to the results of text mining, to obtain the composition of factors affecting consumer satisfaction. The research shows that this study not only improves the reliability of determining key factors, but also provides management implications for cosmetic e-commerce enterprises in four aspects: product traits, usage experience, brand value, and purchase intention, respectively. The study shows that this study not only improves the reliability of determining key factors, but also provides management implications for the improvement of cosmetic e-commerce enterprises in four aspects: product traits, usage experience, brand value, and purchase intention.