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The growth of information and technology is causing individuals to live a more digital lifestyle, and one example of this is in the buying and selling activities that already use E-Commerce as a venue to facilitate them. Using the RStudio application and the Naive Bayes Classifier Algorithm, this study aims to find or analyze both positive and negative reviews of the e-commerce application through user reviews on Google Play. The technique employed is text mining and text processing, which involves stemming, tokenizing, case folding, normalization, and filtering steps. Review data for the Shopee app on Google Play is gathered through data scraping utilizing the web application appfollow, and review data may be saved in csv format. The acquired data will be processed using text processing, first by translating reviews in other languages to Indonesian, then by normalizing or cleaning the content to remove emoticons. The normalized data will then be uniformly converted to lower case letters as a whole in the case folding process, which comes next. Each word that has no influence or is independent, which is referred to as a token, will be isolated from the uniform data. Calculating the presence of words in the data and their frequency of occurrence is made easier by the tokenizing procedure. The Nave Bayes Classifier Method is used to compare training data and test data after text has undergone text processing to produce positive and negative sentiment classes based on the number of word frequencies.
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