Sentiment Analysis Regarding the Indonesian House of Representatives Rejecting the Constitutional Court Decision from Social Media Using Naive Bayes - Fahar Abdul Aziz; Lailan Sofinah Harahap (JEECS (Journal of Electrical Engineering and Computer Sciences), 2025)
This study analyzes public sentiment towards the HOR's rejection of the Constitutional Court's decision regarding the age limit for regional head candidates. Data was obtained from TikTok comments using scraping techniques with the Apify platform, resulting in 574 comments being analyzed. Sentiment labeling was automatically used VADER (Valence Aware Dictionary and Sentiment Reasoner), with positive, neutral, and negative sentiment categories. Text representation was carried out using TF-IDF, and sentiment classification using the Naive Bayes algorithm. The analysis results showed that most comments were neutral (42.0%) and positive (41.8%), while negative sentiment was only 16.2%. This study provides important insights into public perceptions of political issues involving the HoR and Constitutional Court decisions. By analyzing sentiment through comment data on TikTok, this study shows that lexicon-based approaches such as VADER can be used for automatic sentiment labeling, saving time compared to manual methods. In addition, classical algorithms such as Naive Bayes, combined with TF-IDF text representation, have proven effective in handling sentiment classification for short and informal texts such as social media comments.