Abstract
Online shopping has become a popular shopping method ever since the number of internet users increased. Online shopping activities have become very easy and flexible because they can be completed anywhere and anytime. The products provided are also complete. The products sold often do not always match the actual conditions because the product can only be seen through pictures. Users who have purchased a product can share their opinions using the review feature. However, the products purchased thousands or millions of times have many reviews. To take an overview of the product, it is essential to go through every positive and negative review, which takes a lot of time and effort. Reviews of products from the Shopee marketplace will be classified into positive or negative sentiments towards women's home wear clothing or house dress in this study. The research starts with data crawling, text preprocessing, training data, testing, and evaluation model and then concludes with a general description based on the most frequently discussed topics in the reviews for each sentiment class. Classification is done using the Naïve Bayes Classifier algorithm. The accuracy obtained is 90,03%. The total dataset is 2907 data.
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