Analisis Sentimen Aplikasi Spotify Pada Ulasan Pengguna di Google Play Store Menggunakan Metode Support Vector Machine - Wulandari, Cindi; Sunardi, Lukman; Hasbiana, Hasbiana (KLIK: Kajian Ilmiah Informatika dan Komputer, 2024)
The Spotify app makes it easy for users to listen to their favorite songs. Usually the Spotify App is accessed on a smartphone so that it can be played at any time.  Today's digital generation can use technology in the form of music, music can affect human feelings and thoughts. The increasing number of Spotify application users on the Google Play Store, raises a variety of user reviews of the application. These reviews can be in the form of positive or negative comments. Addressing this, it is necessary to conduct sentiment analysis in order to provide a deeper understanding of user perceptions and grouping of user reviews of the Spotify application. Sentiment analysis is a case study of opinions, feelings, and emotions expressed in texs. The number of diverse reviews requires classification of reviews into positive and negative classes using the Support Vector Machine method. The purpose of this research is so that it can be examined to what extent the positive and negative reviews can be used as a reference in building the Spotify application to be even better. Object classification is done based on training data that uses the closest distance or similarity to the object for convenience. Using 5000 relevant review data from December 2023 to January 2024. After the labelling stage is carried out into positive and negative classes, there are 3193 positive and 1347 negative comments. The results of sentiment analysis testing using the Support Vector Machine method resulted in an accuracy of 85%, precision 86%, recall 92% and f1-score 89%.