ANALISIS SENTIMEN ULASAN APLIKASI SHAZAM DI GOOGLE PLAY STORE MENGGUNAKAN SUPPORT VECTOR MACHINE

Abstract

Shazam is a popular music recognition app on the Google Play Store. Shazam allows users to discover new songs and identify songs that are playing around them. In addition, it provides song lyrics, music videos, and song recommendations. User reviews give an idea of how users perceive the application, be it positive or negative. In this study, sentiment analysis was conducted on Shazam user reviews on the Google Play Store. The researchers used a Support Vector Machine (SVM) model to classify user reviews into two sentiments, namely positive and negative. Results show that SVM has 84% accuracy in predicting the sentiment of Shazam reviews. Furthermore, it can be shown that Shazam gets five times more positive responses than negative responses. Therefore, SVM is better at predicting positive reviews than negative reviews. This research can help interested parties to understand how users perceive the app as a whole.