Analysis of Public Perception of the Digital Branding of the “Content Governor” on X Social Media using Naïve Bayes - Febrianti, Dwi; Martanto; Fatihanursari Dikananda; Mulyawan; Irfan Ali (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The development of social media, particularly the X (Twitter) platform, has created a digital discussion space that influences the formation of public opinion on political issues. The “Content Governor” phenomenon, often associated with Dedi Mulyadi, has generated various responses from users, making systematic sentiment analysis necessary to understand these response patterns. This study aims to map public sentiment tendencies while developing a sentiment-classification model using a Naïve Bayes approach combined with TF–IDF weighting. Data were collected through the Twitter API based on several relevant keywords, yielding 2,133 tweets, which, after a selection process and manual labeling, were narrowed down to 1,023 labeled data points consisting of positive, neutral, and negative sentiments. The research stages include preprocessing (case folding, cleaning, normalization, tokenization, stopword removal, and stemming), data balancing using SMOTE, TF–IDF feature construction, and training a Multinomial Naïve Bayes model with an 80:20 data split ratio. Evaluation was carried out using accuracy, precision, recall, F1-score, and a confusion matrix. The results show that the model achieved an accuracy level of 74.15%, with the strongest performance in the negative sentiment category. The distribution of public sentiment also indicates a dominance of negative sentiment (812 items) compared to neutral and positive sentiments, suggesting that netizens’ responses to the “Content Governor” issue tend to be more critical and negative in tone. These findings are expected to enrich studies on digital political communication and provide methodological contributions to sentiment analysis research in the Indonesian language.