Sentiment Analysis of Suicide on X Using Support Vector Machine and Naive Bayes Classifier Algorithms

Background: The World Health Organization (WHO) defines health as a state of physical, mental, and social well-being, not just the absence of disease. Mental health, essential for overall well-being, is often neglected, leading to disorders like depression, a major cause of suicide. In Indonesia, suicide cases have surged, with 971 reported from January to October 2023. Objective: This study aims to analyze public sentiment regarding the rise in suicide cases in Indonesia using sentiment analysis methods, specifically Support Vector Machine (SVM) and Naive Bayes Classifier (NBC). The findings are expected to raise public awareness and provide policy recommendations to support mental health initiatives. Methods: One method used to understand public perception regarding the issue of suicide is text mining. This research employs text mining techniques with the Support Vector Machine (SVM) and Naive Bayes Classifier algorithms to analyze public sentiment related to suicide cases in Indonesia. Data was collected from tweets on social media platform X using crawling methods with snscrape and Python, totaling 1,175 tweets. Results: The results indicate that the Linear SVM model achieved higher accuracy than Naive Bayes in classifying tweet sentiments, with an accuracy rate of 80%. Conclusion: The SVM algorithm with a linear kernel achieved 80% accuracy and an identical ROC-AUC score. Word cloud visualization highlighted terms like "kill," "self," "depression," and "stress" as key negative sentiments. This study aims to raise public awareness and support better mental health policies in Indonesia.