Logo PTI Logo FedCSIS

Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Social Media, Topic Modeling and Sentiment Analysis in Municipal Decision Support

DOI: http://dx.doi.org/10.15439/2023F1479

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1235–1239 ()

Full text

Abstract. Many cities around the world are aspiring to become smart. However, smart initiatives often give little weight to the opinions of average citizens. One reason is the difficulty and high cost of opinion collection process. Social media are one of many sources of citizen opinions. This paper presents a prototype of a framework for processing social media posts with municipal decision-making in mind. The framework consists of a sequence of three steps: (1) determining the sentiment polarity of each social media post (2) extracting topics being discussed in a set of social media posts and creating a mapping between identified topics and individual posts, and (3) aggregating these two pieces of information into a triangular fuzzy number representing the overall sentiment expressed to- wards each topic. Optionally, the triangular fuzzy number can be reduced into a tuple of two real numbers indicating the``amount'' of positive and negative opinion expressed towards each topic. Framework functionality is demonstrated on tweets published from Ostrava, Czechia over a period of about two months. This application illustrates that the resulting TFNs represent sentiment in a richer way, also capturing the diversity or controversy of opinions expressed on social media.

References

  1. C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the surprising behavior of distance metrics in high dimensional space. In J. Van den Bussche and V. Vianu, editors, Database Theory — ICDT 2001, pages 420–434, Berlin, Heidelberg, 2001. Springer Berlin Heidelberg.
  2. K. B. Ahmed, A. Radenski, M. Bouhorma, and M. B. Ahmed. Sentiment analysis for smart cities : State of the art and opportunities. In Int’l Conf. Internet Computing and Internet of Things (ICOMP’16), 2016.
  3. D. M. Blei, A. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993–1022, 2001.
  4. R. Churchill and L. Singh. The Evolution of Topic Modeling. ACM Computing Surveys, 54(10s):1–35, Jan. 2022.
  5. M. Dahbi, R. Saadane, and S. Mbarki. Social media sentiment monitoring in smart cities: an application to Moroccan dialects. In Proceedings of the 4th International Conference on Smart City Applications, pages 1–6, Casablanca Morocco, Oct. 2019. ACM.
  6. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.
  7. Z. Drus and H. Khalid. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review. Procedia Computer Science, 161:707–714, 2019.
  8. M. Grootendorst. Bertopic: Neural topic modeling with a class-based tf-idf procedure, 2022.
  9. G. R. Halegoua. Smart cities. The MIT Press, 2020.
  10. H. M. Ismail, B. Belkhouche, and N. Zaki. Semantic Twitter sentiment analysis based on a fuzzy thesaurus. Soft Computing, 22(18):6011–6024, Sept. 2018.
  11. C. Jefferson, H. Liu, and M. Cocea. Fuzzy approach for sentiment analysis. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1–6, Naples, Italy, July 2017. IEEE.
  12. P. Kherwa and P. Bansal. Topic Modeling: A Comprehensive Review. ICST Transactions on Scalable Information Systems, 0(0):159623, July 2018.
  13. G. Klir and B. Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall PTR, 1995.
  14. M. Li, E. Ch’ng, A. Chong, and S. See. The new eye of smart city: Novel citizen Sentiment Analysis in Twitter. In 2016 International Conference on Audio, Language and Image Processing (ICALIP), pages 557–562, Shanghai, China, July 2016. IEEE.
  15. L. McInnes, J. Healy, and J. Melville. Umap: Uniform manifold approximation and projection for dimension reduction, 2020.
  16. I. Vayansky and S. A. Kumar. A review of topic modeling methods. Information Systems, 94:101582, Dec. 2020.
  17. T. M. Vinod Kumar, editor. Smart Economy in Smart Cities: International Collaborative Research: Ottawa, St.Louis, Stuttgart, Bologna, Cape Town, Nairobi, Dakar, Lagos, New Delhi, Varanasi, Vijayawada, Kozhikode, Hong Kong. Advances in 21st Century Human Settlements. Springer Singapore, Singapore, 2017.
  18. K. S. Willis. Whose Right to the Smart City? In P. Cardullo, C. Di Feliciantonio, and R. Kitchin, editors, The Right to the Smart City, pages 27–41. Emerald Publishing Limited, June 2019.
  19. F. Zapletal, M. Hudec, M. Švaňa, and R. Němec. Three-level model for opinion aggregation under hesitance. Soft Computing, Feb. 2023.