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All Journal Al Hanin Jurnal Ekonomi dan Kewirausahaan West Science West Science Interdisciplinary Studies West Science Interdisciplinary Studies West Science Social and Humanities Studies INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER) The Eastasouth Journal of Social Science and Humanities Journal of Computer Science Advancements
Usup, Usup
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Sentiment Analysis on Social Media Using Data Mining for Mapping Community Satisfaction Usup, Usup; Sahirin, Rohmat; Lucas, Laura; Qingjun, Chu
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1536

Abstract

Social media has become a significant platform for individuals to express opinions, including satisfaction and dissatisfaction with services and policies, making it a valuable source of community sentiment data. Understanding public sentiment can assist policymakers and organizations in responding to community needs effectively. This study aims to conduct sentiment analysis on social media using data mining techniques to map community satisfaction levels. By analyzing sentiment patterns, this research seeks to provide actionable insights for improving public services and enhancing community engagement. The research applies data mining methodologies, including text mining and machine learning algorithms, to analyze posts and comments collected from various social media platforms. Sentiment classification was performed using natural language processing (NLP) and a supervised machine learning approach to categorize sentiments as positive, neutral, or negative. The model was trained on a large dataset and validated to ensure accuracy in sentiment detection. Results indicate that social media sentiment analysis can reliably reflect community satisfaction trends, with findings showing 70% positive, 15% neutral, and 15% negative sentiments regarding local services. The study concludes that data mining for sentiment analysis provides a robust method for assessing community satisfaction on social media, offering a real-time understanding of public opinion. By implementing this approach, organizations and policymakers can identify areas of improvement and proactively address community concerns, ultimately fostering a responsive and community-centered approach to public service.  
Co-Authors Ahmad Nur Budi Utama Arief Yanto Rukmana Chalid, Nurbani Frans Sudirjo Harti, Umi Judijanto, Loso Kurniawan, Nur Hasan Lesmana, Tera Lucas, Laura Nanang Qosim Pahrijal, Rival Purwaningsih, Dewi Ismu Qingjun, Chu Sahirin, Rohmat Soelistianto, Farida Arinie Sudarsono , Sudarsono Titik Haryanti