ANALISIS SENTIMEN PRODUK KECANTIKAN JENIS MOISTURIZER DI TWITTER MENGGUNAKAN ALGORITMA SUPER VECTOR MACHINE - Ria Cantika Larasati; Christine Dewi; Henoch Juli Christanto (Jurnal Tekinkom (Teknik Informasi dan Komputer), 2024)
The increasing public awareness in using beauty products as part of their lifestyle has gone hand in hand with the increasing public views on these products which are often expressed through reviews on social media platforms. This research reviews the use of the Support Vector Machine algorithm to classify sentiment towards the use of moisturizer on Application X. This research was conducted to obtain information on people’s responses to various moisturizer products that are spreading in the community. The data used in this study were 50,868 opinion tweets, 35,565 tweets were used as training data and 15,303 tweets were used as test data. In sentiment labeling using the TextBlob library. The classification result using Support Vector Machine get accuracy value of 98%, the highest confusion matrix of 98%, the highest recall value of 100%, and an f1-score value of 99%.