Twitter Public Sentiment towards Vasectomy in Indonesia Using SVM and Naïve Bayes - Febrilliani, Jihan Sastri; Wibowo, Arief (LifeSciFi, 2025)
Social media has experienced rapid growth in various sectors, including the health sector. Active users of social media or often called netizens, are quite active in the health community by sharing information and experiences. access to social media is the main means of finding information related to health. This study aims to find out public sentiment on the issue of vasectomy using the SVM and Naïve Bayes methods. This research collects data from social media platforms (X) or Twitter, using several keywords related to vasectomy which has recently become a hot topic in the Indonesian community. The results of this study showed that using the Support Vector Machine (SVM) algorithm and Naïve Bayes as a comparison obtained an accuracy performance of 71% for the support vector machine method and 51% for the naïve bayes method. The findings provide insights for policymakers and health advocates on addressing misconceptions and stigma surrounding vasectomy. The findings suggest to develop a more comprehensive Indonesian sentiment dictionary, use multi-platform data, and apply advanced NLP techniques to better capture cultural and contextual nuances.