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Potential Data Sources for Sentiment Analysis Tools for Municipal Management Based on Empirical Research

  • Dorota Jelonek
  • Agata Mesjasz-Lech
  • Cezary Stępniak
  • Tomasz Turek
  • Leszek ZioraEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

The paper addresses the issue of the possibility of using sentiment analysis tools to support the management of local government entities. The author team carried out research in this field in several dozen municipalities in the northern part of the Silesian Voivodship. Among the analyzed issues, the information resources which are or should be collected for the purpose of managing municipalities were analyzed. The issue of municipal management was divided into three aspects: management of municipal offices, management of commune assets and resources, and community management. The paper presents sentiment analysis tools and a description of potential information resources that are or should be collected. The conclusions indicated fundamental barriers to the use of sentiment analysis tools for municipal management purpose.

Keywords

Sentiment analysis Municipal management Local government 

References

  1. 1.
    Asghar, M.Z., Khan, A., Ahmad, S., Quasim, M., Khan, I.A.: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLoS ONE 12(2), e0171649 (2017).  https://doi.org/10.1371/journal.pone.0171649CrossRefGoogle Scholar
  2. 2.
    Jelonek, D., Stępniak, C., Ziora, L.: The meaning of big data in the support of managerial decisions in contemporary organizations: review of selected research. In: Proceedings of 2018 Future of Information and Communication Conference, Singapore. IEEE New York, pp. 195–198 (2018)Google Scholar
  3. 3.
    Farhadloo, M., Rolland, E.: Fundamentals of sentiment analysis and its applications. In: Pedrycz, W., Chen, S.M. (eds.) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol. 639. Springer International Publishing, Switzerland (2016)Google Scholar
  4. 4.
    Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012). www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf. Accessed 10 May 2018
  5. 5.
    Ziora, L.: The Sentiment Analysis as a Tool of Business Analytics in Contemporary Organizations. Economics Studies. University of Economics in Katowice Research Papers, no 281, Katowice, pp. 234–241 (2016)Google Scholar
  6. 6.
    Goss, M., Głowacka, N.: Introduction to sentiment analysis (in Polish), goo.gl/p 1I3mD. Accessed 10 May 2018Google Scholar
  7. 7.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Shams Eng. J. 5(4), 1093–1113. www.sciencedirect.com/science/article/pii/S2090447914000550. Accessed 10 May 2018CrossRefGoogle Scholar
  8. 8.
    Tomanek, K.: Sentiment analysis – method of qualitative data analysis. An example of application and evaluation of RID glossary and Bayes classification method in qualitative data analysis. In: Review of Qualitative Sociology (in Polish), vol. 10, no. 2, pp. 118–136. www.przegladsocjologiijakosciowej.org. Accessed 10 May 2018
  9. 9.
    Bing Liu’s Opinion Lexicon. https://goo.gl/9rNgVy. Accessed 10 May 2018
  10. 10.
    Sentiwordnet.isti.cnr.it. Accessed 10 May 2018Google Scholar
  11. 11.
    http://mpqa.cs.pitt.edu. Accessed 10 May 2018
  12. 12.
    Sentiment Symposium Tutorial: Lexicons. http://sentiment.christopherpotts.net/lexicons.html. Accessed 10 May 2018
  13. 13.
    Sentiment Analysis with Python NLTK Text Classification. http://text-processing.com/demo/sentiment. Accessed 10 May 2018
  14. 14.
    www.sentiment140.com. Accessed 10 May 2018
  15. 15.
    goo.gl/qdeac. Accessed 10 May 2018Google Scholar
  16. 16.
    Saif, H., He, Y., Fernandez, M., Alani, H.: Semantic Patterns for Sentiment Analysis of Twitter. Springer, Heidelberg. https://link.springer.com/content/pdf/10.1007%2F978-3-319-11915-1_21.pdf
  17. 17.
    Ren, F., Wang, L.: Sentiment analysis of text based on three-way decisions. J. Intell. Fuzzy Syst. 33, 245–254 (2017)CrossRefGoogle Scholar
  18. 18.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), Portland, Oregon, pp. 30–38, 23 June 2011Google Scholar
  19. 19.
    www.sas.com. Accessed 10 May 2018
  20. 20.
    Dzieciątko, M.: Text Mining and Everyday Reality. www.sas.com. Accessed 10 May 2018
  21. 21.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4) (2013)CrossRefGoogle Scholar
  22. 22.
    Liu, Z., Ng, E.Y.K.: When siri knows how you feel: study of machine learning in automatic sentiment recognition from human speech. In: Proceedings of Future of Information and Communication Conference (FICC), Singapore, pp. 661–665 (2018)Google Scholar
  23. 23.
    Jelonek, D., Stępniak, C., Turek, T.: The concept of building regional business spatial community. In: 10th International Joint Conference on e-Business and Telecommunications. Proceedings, ICETE 2013, Reyklavik, Iceland, 29–31 July, SCITEPRESS—Science and Technology Publications, pp. 83–90 (2013)Google Scholar
  24. 24.
    Antonopoulos, Ch.G., Shang, Y.: Opinion formation in multiplex networks with general initial distributions. Nat. Sci. Rep. 8, Article Number 2852 (2018). https://www.nature.com/articles/s41598-018-21054-0

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dorota Jelonek
    • 1
  • Agata Mesjasz-Lech
    • 1
  • Cezary Stępniak
    • 1
  • Tomasz Turek
    • 1
  • Leszek Ziora
    • 1
    Email author
  1. 1.Faculty of ManagementCzestochowa University of TechnologyCzestochowaPoland

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