Published: 2024-12-01
Sentiment Analysis of Social Media X Users Towards Legislators Engaged in Online Gambling Using Naïve Bayes Algorithm
DOI: 10.35870/ijsecs.v4i3.3079
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Abstract
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.
Keywords
Sentiment Analysis ; Twitter ; Online Gambling ; Legislative Members ; Naïve Bayes ; RapidMiner
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 3 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i3.3079
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Vivi Nurmaylina
Information Systems Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
Yuma Akbar
Information Systems Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia
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