Attitudes Toward Feminism in Ukraine: A Sentiment Analysis of Tweets

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 871)


This paper presents a sentiment analysis of Ukrainian tweets on feminism. In order to carry out a computational study of opinions, we have adjusted the SentiStrength algorithm to the Ukrainian language by replacing the English term lists in the program files with the Ukrainian ones. The main contribution is an attempt to compile a social domain sentiment lexicon for Ukrainian (3,736 words). The SentiStength output has shown a prevailing negative sentiment of the analyzed tweets. The program performance was evaluated in terms of accuracy, precision, recall, error, fallout and F1 Score. In addition, we found a number of common attributes of a feminist, which also predominantly express negative attitude. Overall, the findings show that a direct support of a key feminist goal, i.e. equality of women and men in society, by the Ukrainian Tweeter users couples with misconception about the concept of feminism and unwillingness to be called a feminist.


Sentiment analysis SentiStrength Ukrainian sentiment lexicon Feminism 


  1. 1.
    Romanyshyn, M.: Rule-based sentiment analysis of Ukrainian reviews. Int. J. Artif. Intell. Appl. (IJAIA) 4(4), 103 (2013)Google Scholar
  2. 2.
    Lobur, M., Romaniuk, A., Romanyshyn, M.: Defining an approach for deep sentiment analysis of reviews in Ukrainian. Visnyk Natsionalnogo Universytetu Lvivska Politehnika. Komputerni systemy proektuvannia, Teoria i praktyka 747, pp. 124–130 (2012)Google Scholar
  3. 3.
  4. 4.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  5. 5.
    Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)Google Scholar
  6. 6.
    Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1966)Google Scholar
  7. 7.
    Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon, pp. 1083–1086 (2004)Google Scholar
  8. 8.
    Agerri, R., García-Serrano, A.: Q-WordNet: Extracting polarity from WordNet senses.
  9. 9.
    Baccianella, S., Esuli, A., Sebastiani, F., SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining.
  10. 10.
    Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, pp. 417–424 (2002)Google Scholar
  11. 11.
    Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning Subjective Language. Computat. Linguist. 30(3), 277–308 (2004)CrossRefGoogle Scholar
  12. 12.
    Choi, Y., Cardie, C.: Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 793–801 (2008)Google Scholar
  13. 13.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 544–2558 (2012)Google Scholar
  14. 14.
    Hamilton, W. L., Clark, K., Leskovec, J., Jurafsky, D.: Inducing domain-specific sentiment lexicons from unlabeled corpora. In: Empirical Methods in Natural Language Processing (EMNLP) (2016)Google Scholar
  15. 15.
    Yang, Y., Eisenstein, J.: Putting things in context: community-specific embedding projections for sentiment analysis.
  16. 16.
    Olson, D.L., Dursun, D.: Advanced Data Mining Techniques, 1st edn. Springer, Heidelberg (2008)Google Scholar
  17. 17.
    Kis, O.: Who is not protected by the Berehynya, or matriarhy as a male invention. 4(16), 11–16 (2006)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Applied Linguistics DepartmentLviv Polytechnic National UniversityLvivUkraine

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