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TY  - JOUR
AU  - Zulzila, Alivia
AU  - Latifah Jayatri Febiola, 
AU  - Dodi Vionanda, 
PY  - 2025/02/28
Y2  - 2026/09/28
TI  - Analysis of Public Sentiment towards Corruption Based on Tweets Using Naive Bayes Classifier
JF  - UNP Journal of Statistics and Data Science
JA  - ujsds
VL  - 3
IS  - 1
SE  - Articles
DO  - 10.24036/ujsds/vol3-iss1/345
UR  - https://ujsds.ppj.unp.ac.id/index.php/ujsds/article/view/345
SP  - 72-78
AB  - &lt;p&gt;&lt;em&gt;Corruption is one of the big problems faced in Indonesia. The still high rate of corruption can damage the integrity of government, hamper economic growth, and reduce public trust in public institutions. Even though the government has made efforts to eradicate corruption, such as the formation of the Corruption Eradication Commission (KPK), these big challenges remain. Social media, especially Twitter, has become an important platform for people to voice opinions and criticize corruption issues. Sentiment analysis is used to detect opinions in the form of judgments, evaluations, attitudes and emotions of a person. The textual classification algorithm used in this research is Naive Bayes. This research aims to determine public sentiment towards corruption in Indonesia in positive, negative and neutral categories. This is done by data preprocessing, data labeling, and classification. The results of sentiment classification using the Naïve Bayes method obtained positive sentiment of 11, negative sentiment of 14, and neutral sentiment of 1485. So it can be concluded that Indonesian society tends to have neutral sentiments towards corruption that occurs in Indonesia&lt;/em&gt;&lt;/p&gt;
ER  -