Analysis of Public Sentiments and Emotions in the Government Domain

  • 1 Faculty of Economics and Business Administration
  • 2 Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria


Digitalization of public services is a key component in the development of e-government. Citizens’ opinion, needs and overall satisfaction with the provided services are key to the successful implementation of digital transformation across the government sector. Hence, for policy makers it is highly important to develop efficient methods for analyzing public opinion. The main aim of this paper is to use unconventional apparatus to mine public opinion in the Bulgarian government sector. The proposed approach relies on the application of natural language processing and text mining techniques. The study aims at discovering the sentiments and emotions freely expressed by citizens in popular social media websites in Bulgaria. Several topics of public interest are analyzed and insights on public perception of aspects of Bulgarian e-government are derived. Furthermore, benefits and limitations of using sentiment lexicons for mining public opinion are outlined.



  1. United Nations. “E-Government in Support of Sustainable Development.” UN E-Government Survey, (2016).
  2. KPMG, "Standing firm on shifting sands. Global banking M&A outlook H2 2020," (2020).
  3. Rongxuan, S., Bin, Z., and Jianing, M., “End-to-End Aspect-Level Sentiment Analysis for E-Government Applications Based on BRNN”, Data Analysis and Knowledge Discovery, 6(2/3), 364-375, (2022).
  4. N‟Diaye, A. C. M., Chrif, M. E. M. E. A., El Mahmoud, B. M., and El Beqqali, O., “Apply sentiment analysis technology in social media as a tool to enhance the effectiveness of e-government: Application on Arabic and Mauritanian dialect „HASSANIYA‟.”, In 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS) (pp. 1-5). IEEE, (2021, October).
  5. Chakraborty, A., “IDENTIFICATION OF PUBLIC SENTIMENT OVER COMMENTS THROUGH TWEETS BY DIGITAL INDIA”, PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 9661- 9694, (2020).
  6. Alguliyev, R. M., Aliguliyev, R. M., and Niftaliyeva, G. Y., “Extracting social networks from e-government by sentiment analysis of users' comments”, Electronic Government, an International Journal, 15(1), 91-106, (2019).
  7. Al-Qudah, D. A., Ala‟M, A. Z., Castillo-Valdivieso, P. A., and Faris, H., “Sentiment analysis for e-payment service providers using evolutionary extreme gradient boosting”, IEEE Access, 8, 189930- 189944, (2020).
  8. Pang, B., and Lee, L., “Opinion mining and sentiment analysis”, Foundations and Trends® in information retrieval, 2(1–2), 1-135, (2008).
  9. Yadollahi, A., Shahraki, A. G., and Zaiane, O. R., “Current state of text sentiment analysis from opinion to emotion mining”, ACM Computing Surveys (CSUR), 50(2), 1-33, (2017).
  10. Liu, B., “Sentiment analysis and opinion mining”, Springer Nature, (2022).
  11. Mohammad, S. M., and Turney, P. D., “Crowdsourcing a word– emotion association lexicon”, Computational intelligence, 29(3), 436- 465, (2013).
  12. Baccianella, S., Esuli, A., and Sebastiani, F., “Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining”. In LREC, vol. 10(2010), 2200-2204, (2010).
  13. Hutto, C., and Gilbert, E., “Vader: A parsimonious rule-based model for sentiment analysis of social media text”, In Proceedings of the international AAAI conference on web and social media, vol. 8(1), 216-225, (2014).
  14. Nielsen, F. Å., “A new ANEW: Evaluation of a word list for sentiment analysis in microblogs”, arXiv preprint arXiv:1103.2903, (2011).
  15. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ..., and Polosukhin, I., “Attention is all you need”, Advances in neural information processing systems, 30, (2017).
  16. Kapukaranov, B., and Nakov, P., “Fine-grained sentiment analysis for movie reviews in Bulgarian”, In Proceedings of the RANLP, pp. 266- 274, (2015).

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