ANALISIS SENTIMEN TERHADAP PRODUK SKIN GAME DI FORUM REVIEW FEMALE DAILY MENGGUNAKAN METODE MULTINOMIAL NAÏVE BAYES DAN TF-IDF

Female Daily is an online platform that focuses on beauty, including cosmetics. This platform has a review forum with more than 50,000 cosmetic products that can be reviewed by their members. One of the brands on this platform is Skin Game, a local cosmetic brand that was founded in 2019 and provide...

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Bibliographic Details
Main Author: Quina Alifa Nanda Prakoso, (Author)
Format: Book
Published: 2022-07-20.
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520 |a Female Daily is an online platform that focuses on beauty, including cosmetics. This platform has a review forum with more than 50,000 cosmetic products that can be reviewed by their members. One of the brands on this platform is Skin Game, a local cosmetic brand that was founded in 2019 and provides skincare and make-up products. In the process of choosing skincare, prospective buyers often need people's opinions, therefore Female Daily is here to meet those needs. However, with the many reviews available, users need more time to decide to buy a product because they have to read several reviews first. Another way that can be done to make this easier is the classification of sentiment polarity in product reviews. In this study, 450 reviews of Acne Warrior products from the Skin Game brand were taken through the Female Daily review forum and then classified using the Multinomial Naïve Bayes and TF-IDF methods to determine the sentiment. The results of the evaluation of the Multinomial Naïve Bayes model in this study obtained 86.6% accuracy, 89.1% precision, 94.2% recall, and 60% specificity. The result of this research is the polarity of sentiment towards Skin Game products. Based on the results of labelling, out of 450 reviews, 342 reviews (76%) have positive sentiments which means that they are suitable and feel the benefits of the product and 108 reviews (24%) have negative sentiments which means that they are not suitable or the Acne Warrior Skin Game product does not meet user expectations. 
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