Sentiment Analysis of Public Opinion on Facebook Monetization in Social Media Using the SVM Algorithm - Nurmaiyah, Nurmaiyah; Lubis, Aidil Halim (TIN: Terapan Informatika Nusantara, 2025)
Sentiment analysis on Facebook’s monetization policy has become a significant topic in the era of rapid digital transformation. This study examines public opinion on the policy by analyzing TikTok user comments that specifically discuss Facebook monetization. TikTok was chosen as the data source because it reflects spontaneous and real-time public reactions, including discussions about other platform policies. A total of 5,000 TikTok comments were collected using web scraping techniques. The data underwent several preprocessing stages, including text cleaning, tokenization, normalization, stopword removal, and stemming. Sentiment labeling was carried out using the Indonesian Sentiment Lexicon (InSet), while feature extraction employed the Term Frequency–Inverse Document Frequency (TF-IDF) method. The classification process was conducted using the Support Vector Machine (SVM) algorithm with a linear kernel. The dataset was split into training and testing sets with an 80:20 ratio. The classification achieved an accuracy of 80%, with a precision of 80% for both positive and negative sentiments, recall scores of 81% and 79%, and F1-scores of 81% and 79%, respectively. These findings demonstrate that integrating TF-IDF weighting with the SVM algorithm is effective for automatically classifying public sentiment toward social media monetization policies. Furthermore, this study provides insights into public reactions to Facebook monetization from the perspective of TikTok users, thereby contributing to an understanding of how monetization policies influence user sentiment on social media platforms.