<![CDATA[Today, beauty products are essential especially for women. Because they're so popular, review sites now offer information on products. These internet reviews include views on trends, customer satisfaction and product performance. It's hard to draw conclusions from so many reviews. Large amounts of text have been analyzed using topic modelling techniques like Latent Dirichlet Allocation (LDA). LDA is a probabilistic model that explains data and why some sections are similar. LDA is a useful tool for text mining and information retrieval. Recent studies show that topic modelling for product reviews in the cosmetics business can provide useful insights into consumer perceptions and product qualities. The purpose of this study is to examine themes in customer evaluations of ten different brands of face wash products from the female daily website. Data collection, preprocessing, topic modeling using LDA, visualization, and interpretation of topics are all steps in the research procedure. The findings show that Topic 2, which captures user conversations about product benefits that users prefer, is the most frequently discussed topic by users when evaluating a product (48.5%). This is followed by Topic 1, which captures user conversations about the effects of products on acne-prone skin (38%). Finally, Topic 3 captures user conversations about products based on natural ingredients (13.5%). These topics provide valuable insights for both manufacturers, who can improve product offerings, and consumers, helping them make informed purchasing decisions.]]>
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