STUDI BIBLIOMETRIK: PENERAPAN ALGORITMA SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN BERBASIS WEB UNTUK KEPUASAN PELANGGAN - Deni Ramadhan; Defni; Rostam Ahmad Efendi; Yulherniwati (UNIB Press, 2026)
In the digital business era, customer satisfaction is a critical metric often evaluated through internet-based sentiment analysis, where Support Vector Machine (SVM) has emerged as a highly reliable algorithm. This study aims to map existing literature and identify future trends regarding the application of SVM in analyzing online sentiment for customer satisfaction. A bibliometric analysis was conducted using literature metadata from leading databases, with citation and trend analyses visualized via VOSviewer. Our network visualizations reveal key thematic clusters, highlighting a strong correlation between the SVM algorithm and the evaluation of customer reviews. Furthermore, density visualizations successfully identify specific research gaps, particularly concerning the integration of SVM with web-based platforms. These findings offer clear directions for future research. While the foundational applications of SVM have been extensively studied, exploring web-based data approaches still presents significant opportunities for future investigation.