This research aims to compare the performance of the Random Forest and Support Vector Machine methods in analyzing Facebook data sentiment related to traffic congestion in Palembang City. Data was obtained through a web scraping process and labeled using a lexicon-based approach into three sentiment classes, namely positive, negative, and neutral. Feature representation was performed using the …
This study aims to analyze public sentiment toward traffic congestion in Palembang City based on Facebook social media comments using the Naïve Bayes algorithm. Data were collected through web scraping of Facebook comments related to traffic conditions in Palembang and labeled using a lexicon-based approach into three sentiment classes: positive, negative, and neutral. The dataset consists of …