ASPECT-BASED SENTIMENT ANALYSIS OF SKINTIFIC BEAUTY PRODUCT REVIEWS USING A BERT MODEL
Keywords:
Sentiment Analysis, Beauty Product, BERT, ABSAAbstract
Product reviews are essential in e-commerce because they convey customer experiences and evaluate product quality. This is particularly crucial for cosmetics, as subpar quality might cause bodily harm. Customer interest in making a purchase is also increased by reviews. According to earlier studies, product reviews vary in several ways and contain different information, making it difficult for customers to rapidly evaluate them from multiple perspectives. This study employs a BERT model to perform aspect-based sentiment analysis of positive, neutral, and negative aspects in beauty product reviews on Skintific. In addition, the objective is to support customers in selecting the best products by providing more accurate sentiment classification across multiple parameters. To assess several characteristics of product reviews, such as benefits, quality, and price, a review of the circumstances surrounding the reviews was conducted. Overall, the results show that consumers of Skintific beauty products generally rated price, quality, and benefits positively; 64%, 76.1%, and 70.0% of evaluations, respectively, expressed this view. While there are a few unfavourable reviews across categories, positive reviews primarily highlight the product's appropriate and calming ingredients, efficient skin hydration and pore cleansing, and affordable prices with regular sales.
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