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September 10, 2025

Sentiment Analysis of Social Media Towards Public Services Using Naive Bayes and Text Mining

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Naive Bayes algorithm effectively classifies sentiment data as positive, negative, or neutral, enhancing public service evaluation.
The study found a satisfactory level of accuracy in classifying sentiments from social media feedback on public services.
Text mining techniques are applied to preprocess data from social media, involving tokenizing, stopword removal, and stemming.
The insights gained could help government agencies improve public service quality based on user feedback obtained via social media.

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Analysis of public sentiment on quality of public services utilizes Naive Bayes and text mining, indicating effectiveness in real-time monitoring.

Saragih et al. (2024) studied this question.

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Analysis of public sentiment on quality of public services utilizes Naive Bayes and text mining, indicating effectiveness in real-time monitoring.