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- Vol. 5 Issue 3
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Urban Intel: AI-Based Public Issue Detection from Social Media for Municipal Decision Support
Authors
1 Assistant Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India. 2 3 4 5 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 700-708
- Article Type
- Original Article
References
1. UrbanIntel — Public Issue Detection System, project web application. https://urban-intel-virid.vercel.app/ (accessed 27 October 2026).
2. UrbanIntel — Citizen interface. https://urban-intel-virid.vercel.app/citizen (accessed 27 October 2026).
3. React, “React Documentation.” https://react.dev/
4. React Router, “React Router Documentation.” https://reactrouter.com/
5. Recharts, “Recharts Documentation.” https://recharts.org/
6. Leaflet, “Leaflet Documentation.” https://leafletjs.com/7. Reddit, “Reddit API Documentation.” https://www.reddit.com/dev/api/
8. Kaur M, Singh H. An Approach for Extracting Urban Complaints from Social Media. In: International Conference on Computational Intelligence, Communication, and Control. Springer; 2020. p. 345–356.
9. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention Is All You Need. In: Advances in Neural Information Processing Systems. 2017. p. 5998–6008.
10. Lewis DD. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In: European Conference on Machine Learning.Springer; 1998. p. 4–15.
11. McCallum A, Nigam K. A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI/ICML-98 Workshop on Learning for Text Categorization. 1998;752:41–48.
12. Bird S, Klein E, Loper E. Natural Language Processing with Python. O’Reilly Media; 2009.
13. Zhang X, Zhao J, LeCun Y. Character-level Convolutional Networks for Text Classification. In: Advances in Neural Information Processing Systems. 2015. p. 649–657.
14. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of NAACL-HLT. 2019. p. 4171–4186.
2. UrbanIntel — Citizen interface. https://urban-intel-virid.vercel.app/citizen (accessed 27 October 2026).
3. React, “React Documentation.” https://react.dev/
4. React Router, “React Router Documentation.” https://reactrouter.com/
5. Recharts, “Recharts Documentation.” https://recharts.org/
6. Leaflet, “Leaflet Documentation.” https://leafletjs.com/7. Reddit, “Reddit API Documentation.” https://www.reddit.com/dev/api/
8. Kaur M, Singh H. An Approach for Extracting Urban Complaints from Social Media. In: International Conference on Computational Intelligence, Communication, and Control. Springer; 2020. p. 345–356.
9. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention Is All You Need. In: Advances in Neural Information Processing Systems. 2017. p. 5998–6008.
10. Lewis DD. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In: European Conference on Machine Learning.Springer; 1998. p. 4–15.
11. McCallum A, Nigam K. A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI/ICML-98 Workshop on Learning for Text Categorization. 1998;752:41–48.
12. Bird S, Klein E, Loper E. Natural Language Processing with Python. O’Reilly Media; 2009.
13. Zhang X, Zhao J, LeCun Y. Character-level Convolutional Networks for Text Classification. In: Advances in Neural Information Processing Systems. 2015. p. 649–657.
14. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of NAACL-HLT. 2019. p. 4171–4186.
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