Sentiment Analysis on Social Media Using Data Mining for Mapping Community Satisfaction
Article Sidebar
-
Community Satisfaction, Data Mining, Public Opinion, Sentiment Analysis, Social Media
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.
Full text article
References
Aamir, M. (2024). A Comparative Study of ML and DL Approaches for Twitter Sentiment Classification. 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024, Query date: 2025-02-26 14:17:10. https://doi.org/10.1109/ICEECT61758.2024.10738938
Alqithami, S. (2025). Integrating Sentiment Analysis and Reinforcement Learning for Equitable Disaster Response: A Novel Approach. Sustainability (Switzerland), 17(3). https://doi.org/10.3390/su17031072
Balakrishnan, A. (2021). Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience. Health Informatics Journal, 27(2). https://doi.org/10.1177/14604582211007537
Berrajaa, A. (2022). Natural Language Processing for the Analysis Sentiment using a LSTM Model. International Journal of Advanced Computer Science and Applications, 13(5), 777–785. https://doi.org/10.14569/IJACSA.2022.0130589
Bogdanova, A. V. (2022). Dynamics of Students’ Opinions in the Context of the Transition to Online Learning Based on Social Network Data. Vysshee Obrazovanie v Rossii, 31(6), 77–91. https://doi.org/10.31992/0869-3617-2022-31-6-77-91
Budaya, I. G. B. A. (2023). Evaluation of Public Health Centers Performance through Sentiment Analysis using LSTM in Bali Province, Indonesia. 2023 11th International Conference on Cyber and IT Service Management, CITSM 2023, Query date: 2025-02-26 14:17:10. https://doi.org/10.1109/CITSM60085.2023.10455607
Chen, Y. (2021). Analysis of Community Interaction Modules of European and American Universities. Journalism and Media, 2(2), 129–154. https://doi.org/10.3390/journalmedia2020009
Chen, Y. J. (2024). Online Information-Based Product Evolution Course Mining and Prediction. International Journal of Information Technology and Decision Making, 23(2), 599–627. https://doi.org/10.1142/S0219622023500244
Cope, M. R. (2023). It Is Home: Perceptions, Community, and Narratives about Change. Social Sciences, 12(2). https://doi.org/10.3390/socsci12020081
Dennehy, D. (2023). Sustaining Open Source Communities by Understanding the Influence of Discursive Manifestations on Sentiment. Information Systems Frontiers, 25(1), 241–257. https://doi.org/10.1007/s10796-020-10059-8
Du, Y. (2023). A Textual Data-Oriented Method for Doctor Selection in Online Health Communities. Sustainability (Switzerland), 15(2). https://doi.org/10.3390/su15021241
Elangovan, D. (2022). A NOVEL SENTIMENT CLASSIFICATION MODEL USING GRASSHOPPER OPTIMIZATION ALGORITHM WITH BIDIRECTIONAL LONG SHORT TERM MEMORY. Journal of Theoretical and Applied Information Technology, 100(2), 423–437.
El-Diraby, T. (2019a). Linking social, semantic and sentiment analyses to support modeling transit customers’ satisfaction: Towards formal study of opinion dynamics. Sustainable Cities and Society, 49(Query date: 2025-02-26 13:54:24). https://doi.org/10.1016/j.scs.2019.101578
El-Diraby, T. (2019b). Linking social, semantic and sentiment analyses to support modeling transit customers’ satisfaction: Towards formal study of opinion dynamics. Sustainable Cities and Society, 49(Query date: 2025-02-26 14:13:18). https://doi.org/10.1016/j.scs.2019.101578
Fauzi, R. (2024). Exploring User Satisfaction for Health Application Among Pregnant Women in Indonesia: A Case Study with Aspect Based Sentiment Analysis. 8th International Conference on Information Technology 2024, InCIT 2024, Query date: 2025-02-26 14:17:10, 502–507. https://doi.org/10.1109/InCIT63192.2024.10810583
Ferreira, I. (2019). A longitudinal study on the maintainers’ sentiment of a large scale open source ecosystem. Proceedings - 2019 IEEE/ACM 4th International Workshop on Emotion Awareness in Software Engineering, SEmotion 2019, Query date: 2025-02-26 14:13:18, 17–22. https://doi.org/10.1109/SEmotion.2019.00011
Finnigan, K. D. C. (2022). Weighted Lexicon-based Sentiment Analysis for Women Career Traits in Information Technology. Proceedings of 2022 IEEE 21st International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022, Query date: 2025-02-26 14:17:10, 91–98. https://doi.org/10.1109/ICCICC57084.2022.10101520
Hossain, M. A. (2019). Study of social media activity of local traffic police department: Their posting nature, interaction, and reviews of the public. Advances in Intelligent Systems and Computing, 812(Query date: 2025-02-26 14:17:10), 221–235. https://doi.org/10.1007/978-981-13-1540-4_22
Ikhlas, R. Z., Japakiya, R., & Muzayanah, T. (2023). Utilization of Canva Application as a Learning Media Video Creation. Journal of Social Science Utilizing Technology, 1(3), 158–169. https://doi.org/10.55849/jssut.v1i3.558
Imran, M. (2022). Analysis of Learner’s Sentiments to Evaluate Sustainability of Online Education System during COVID-19 Pandemic. Sustainability (Switzerland), 14(8). https://doi.org/10.3390/su14084529
Jiang, M. (2022). On the effectiveness of testing sentiment analysis systems with metamorphic testing. Information and Software Technology, 150(Query date: 2025-02-26 14:17:10). https://doi.org/10.1016/j.infsof.2022.106966
Krishtal, M. M. (2024). Digital Footprint: Assessing Student Satisfaction with Education Quality. Vysshee Obrazovanie v Rossii, 33(2), 89–108. https://doi.org/10.31992/0869-3617-2024-33-2-89-108
Kulkarni, Y. (2024). Analysing How Marketing Management and Artificial Intelligence are used to Change Customer Engagement. TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024, Query date: 2025-02-26 14:17:10. https://doi.org/10.1109/TQCEBT59414.2024.10545064
Kuutila, M. (2024). What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor. International Symposium on Empirical Software Engineering and Measurement, Query date: 2025-02-26 13:54:24, 371–381. https://doi.org/10.1145/3674805.3686696
Liu, J. (2020). Study on differences between patients with physiological and psychological diseases in online health communities: Topic analysis and sentiment analysis. International Journal of Environmental Research and Public Health, 17(5). https://doi.org/10.3390/ijerph17051508
Mahato, M. (2021). Discourses by Didis of Community Kitchens: A Case on Women Empowerment. South Asian Journal of Business and Management Cases, 10(2), 145–157. https://doi.org/10.1177/22779779211015688
Malik, T. (2021). Social Sensing for Sentiment Analysis of Policing Authority Performance in Smart Cities. Frontiers in Communications and Networks, 2(Query date: 2025-02-26 14:17:10). https://doi.org/10.3389/frcmn.2021.821090
Marchi, D. D. (2022). Tourism Sustainability Index: Measuring Tourism Sustainability Based on the ETIS Toolkit, by Exploring Tourist Satisfaction via Sentiment Analysis. Sustainability (Switzerland), 14(13). https://doi.org/10.3390/su14138049
Park, G. (2023). CredibleExpertRank: Leveraging Social Network Analysis and Opinion Mining to Facilitate Reliable Information Retrieval on Knowledge-Sharing Sites. IEEE Access, 11(Query date: 2025-02-26 14:13:18), 54724–54749. https://doi.org/10.1109/ACCESS.2023.3281412
Primasari, I. (2024). Opinion Mining of Tourism Village in Magelang Based on Google Reviews Data. 2024 International Conference on Data Science and Its Applications, ICoDSA 2024, Query date: 2025-02-26 14:13:18, 189–194. https://doi.org/10.1109/ICoDSA62899.2024.10652083
Putra, P. K. (2023). Exploring Regional Dynamics Through Social Media Conversations and Socio-Economic Surveys: Uncovering Perspectives on the Cianjur Earthquake. 2023 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology: Global Challenges in Geoscience, Electronics, and Remote Sensing: Future Directions in City, Land, and Ocean Sustainable Development, AGERS 2023, Query date: 2025-02-26 13:54:24, 225–230. https://doi.org/10.1109/AGERS61027.2023.10490691
Robin, C. (2020). A term extraction approach to survey analysis in health care. LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, Query date: 2025-02-26 14:17:10, 2069–2077.
Ryan, J. (2021). The psychosocial impacts of covid-19 on a sample of australian adults: Cross-sectional survey and sentiment analysis. JMIR Public Health and Surveillance, 7(7). https://doi.org/10.2196/29213
Sarram, G. (2022). Evaluating the potential of online review data for augmenting traditional transportation planning performance management. Journal of Urban Management, 11(1), 123–136. https://doi.org/10.1016/j.jum.2022.01.001
Satyanarayana, A. (2019). Using natural language processing tools on individual stories from first year students to summarize emotions, sentiments and concerns of transition from high school to college. ASEE Annual Conference and Exposition, Conference Proceedings, Query date: 2025-02-26 14:17:10. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078737537&origin=inward
Shambour, M. K. (2022). Analyzing perceptions of a global event using CNN-LSTM deep learning approach: The case of Hajj 1442 (2021). PeerJ Computer Science, 8(Query date: 2025-02-26 14:17:10). https://doi.org/10.7717/PEERJ-CS.1087
Soussan, T. (2020). Twitter analysis for business intelligence. Advances in Intelligent Systems and Computing, 1035(Query date: 2025-02-26 13:54:24), 473–480. https://doi.org/10.1007/978-3-030-29035-1_46
Sukma, E. A. (2020). Sentiment Analysis of the New Indonesian Government Policy (Omnibus Law) on Social Media Twitter. Proceedings - 2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, Query date: 2025-02-26 14:17:10, 153–158. https://doi.org/10.1109/ICIMCIS51567.2020.9354287
Tetui, M. (2023). Role satisfaction among community volunteers working in mass COVID-19 vaccination clinics, Waterloo Region, Canada. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-15597-9
Wang, Y. (2025). Product Competitive Analysis Model Based on Consumer Preference Satisfaction Similarity: Case Study of Smartphone UGC. Systems, 13(1). https://doi.org/10.3390/systems13010038
Waspodo, B. (2022). Support Vector Machine and Lexicon based Sentiment Analysis on Kartu Prakerja (Indonesia Pre-Employment Cards Government Initiatives). 2022 10th International Conference on Cyber and IT Service Management, CITSM 2022, Query date: 2025-02-26 14:17:10. https://doi.org/10.1109/CITSM56380.2022.9935990
Xie, C. (2020). Data mining and application of social e-commerce users based on big data of internet of things. Journal of Intelligent and Fuzzy Systems, 39(4), 5171–5181. https://doi.org/10.3233/JIFS-189002
Yao, Z. (2022). Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes. International Journal of Environmental Research and Public Health, 19(4). https://doi.org/10.3390/ijerph19042167
Authors
Copyright (c) 2025 Usup Usup, Rohmat Sahirin, Laura Lucas, Chu Qingjun
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.