Abstract
The feedback shared by consumers on e-commerce platforms holds immense value in marketing, as it offers insights into their opinions and preferences, which are readily accessible. However, analyzing a large volume of reviews manually is impractical. Therefore, automating the extraction of essential insights from these data can provide more comprehensive and efficient information. This research focuses on leveraging clustering algorithms to automate the extraction of consumer intentions, related products, and the pros and cons of products from review data. To achieve this, a review dataset was created by performing web crawling on the Naver Shopping platform. The findings are expected to contribute to a more precise understanding of consumer sentiments, enabling marketers to make informed decisions across a wide range of products and services.
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