Ekstraksi Pengetahuan dari Ulasan Aplikasi CapCut Menggunakan Metode Aspect-Based Sentiment Analysis dan Klasifikasi - Ariyani, Ishlah Putri; Tania, Ken Ditha; Wedhasmara, Ari; Meiriza, Allsela (Jurnal Buana Informatika, 2025)
Indonesia is experiencing rapid technological development, especially in the use of the internet and editing platforms like CapCut. These platforms enable video editing on various devices; however, user satisfaction is not always guaranteed due to individual differences in experience. This research aims to identify user sentiment towards the CapCut application based on aspects, using an Aspect-Based Sentiment Analysis (ABSA) approach supported by Machine Learning algorithms for the aspect-based sentiment classification task. The algorithm used in the classification process is Support Vector Machine. The data used are reviews of the CapCut application from the Google Play Store, with a total of 22,668 data points. The results show that the Support Vector Machine (SVM) algorithm performs well in each aspect, with accuracy values of 0.88 for the feature aspect and 0.87 for the user experience aspect. The results of knowledge extraction are obtained in the form of XML, which contains user sentiment information on two main aspects: features and user experience.