Sentiment Analysis of Social Media X Users Towards Legislators Engaged in Online Gambling Using Naïve Bayes Algorithm

Nurmaylina, Vivi
Akbar, Yuma

International Journal Software Engineering and Computer Science (IJSECS)
Vol. 4 No. 3 (2024): DECEMBER 2024

This research analyzes public feelings toward legislative members participating in online gambling applying the Naïve bayes classification technique. The collected data were processed, labeled, cleaned, preprocessed, and classified using RapidMiner Studio software, while conducting the sentiment analysis according to a systematic approach from each of those steps described above, namely, data crawling, cleaning, preprocessing, and classification of the Twitter data. Sentiment distribution yielded 286 negative and 90 positive sentiments with a prediction accuracy of 73.10%. These findings illustrate an overwhelmingly negative public response to this behavior and the expectation society has for legislators as public figures.