Analisis Sentimen Terhadap Kesehatan Mental Remaja Menggunakan Metode Naive Bayes - Syahputra, Pii; Kurniawan, Rakhmat (Forum Kerjasama Pendidikan Tinggi (FKPT), 2024)
Everyday life, especially for teenagers, now involves internet technology. Twitter (now known as X) is one of the most popular social media platforms. Sentiment analysis of social media data can improve people's understanding of mental health problems. This research uses the Naive Bayes algorithm to analyze the sentiments of X social media users regarding adolescent mental health. Another goal of this research is to measure how effective and accurate the technique is in identifying sentiment and presenting analysis results in the form of word clouds and graphs. Data was collected from the beginning of 2024 to the present from tweets with the hashtag Mental Health. The research results show that the Naive Bayes algorithm has an effective level of accuracy in classifying sentiment towards health using the InSetLexicon dictionary. The data preprocessing process also includes cleaning, tokenizing, normalization, stockwords, and stemming. In addition, performance evaluation is carried out using confusion matirx to calculate precision, recall, F-1 Score, and accuracy. The classification results obtained obtained an accuracy of 0.8049792531120332 or around 80%, precision of 83%, recall of 68% and F-1 Score of 74.9%.