Skip to main content
SpringerLink
Log in

Methods for Recognition of Frustration-Derived Reactions on Social Media

  • Conference paper
  • First Online:
Artificial Intelligence (RCAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12948))

Included in the following conference series:

Abstract

In this paper, we attempted to find speech features of different reactions to frustration to detect and classify them in social media texts. Frustration is a highly motivated situation in which it is impossible to achieve a goal when unexpected external or internal obstacles are encountered to meet the need. We use a well-recognized typology of the reactions and focus on context-aware but straightforward models and classification features, which can be easily interpreted. The experiments show that pure lexis cannot be used as the only feature for the classification. Only the models, which combine different-level linguistic features, implicitly like in BERT or in the models with the linguistic patterns, provide fair results. From a psychological point of view, some misclassifications of the obtained reaction data can be related to their assignment to one class of extrapunitive reactions. Discussions in social networks suggest a high level of human activity, a desire to seek a solution to the problem in a broader social interaction. Thus, the focus on extrapunitive reactions and an increased emotional component in the form of aggression is a feature of that interaction type. On the one hand, we provide a method to classify the social network messages; on the other hand, the training results can be interpreted and analyzed by experts in psychodiagnostics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ophir, Y., Tikochinski, R., Asterhan, C.S., Sisso, I., Reichart, R.: Deep neural networks detect suicide risk from textual Facebook posts. Sci. Rep. 10(1), 1–10 (2020)

    Article  Google Scholar 

  2. Devyatkin, D., Chudova, N., Salimovskyi, V.: Method for automated recognition of frustration-derived aggression in texts. In: Velichkovsky, B.M., Balaban, P.M., Ushakov, V.L. (eds.) Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics. AISC, vol. 1358, pp. 663–670. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71637-0_76

    Chapter  Google Scholar 

  3. Devyatkin, D., Kadzhaya, L., Chudova, N., Mishlanov, V., Salimovsky, V.: Automatic identification of cognitive actions constituting speech genres of scientific theoretical text. In: Proceedings of the Linguistic Forum 2020: Language and Artificial Intelligence, Moscow, Russia, 12–14 November, vol. 2852. CEUR Workshop Proceedings (2020)

    Google Scholar 

  4. Rosenzweig, S.: An Outline of Frustration Theory. In: Hunt, V.N.Y. (ed) Personality and Behavior Disorders (1949)

    Google Scholar 

  5. Rosenzweig, S.: The picture-association method and its application in a study of reactions to frustration of personality. J. Pers. 14, 3–23 (1945). https://doi.org/10.1111/j.1467-6494.1945.tb01036.x

    Article  Google Scholar 

  6. L’vova, E., Shlyagina, E., Gusev, A.: Using the Rosenzweig frustration picture test in the study of coping behaviour in the situation of uncertainty. Natl. Psychol. J. 4, 19–27 (2016)

    Article  Google Scholar 

  7. Brubaker, J., Kivran-Swaine, F., Taber, L., Gillian, H.: Grief-stricken in a crowd: the language of bereavement and distress in social media. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, pp. 42–49. The AAAI Press, Palo Alto (2012)

    Google Scholar 

  8. Coppersmith, G., Harman, C., Dredze, M.: Measuring post traumatic stress disorder in Twitter. In: Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM, pp. 579–582 (2014)

    Google Scholar 

  9. Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)

    Article  Google Scholar 

  10. Suri, S., Sharma, K., Papneja, S.: Frustration detection on reviews using machine learning. In: Proceedings of the 2020 International Conference for Emerging Technology (INCET), pp. 1–5. IEEE (2020)

    Google Scholar 

  11. Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on Twitter: a behavioral modeling approach. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 97–106 (2015)

    Google Scholar 

  12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  13. Li, M., Zhao, H., Su, H., Qian, Y., Li, P.: Emotion-cause span extraction: a new task to emotion cause identification in texts. Appl. Intell. 1–13 (2021).https://doi.org/10.1007/s10489-021-02188-7

  14. Devlin J., Chang, M. W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint https://arxiv.org/abs/1810.04805 (2018)

  15. Madukwe, K.J., Gao, X., Xue, B.A.: GA-based approach to fine-tuning BERT for hate speech detection. In: Proceeding of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2821–2828. IEEE (2020)

    Google Scholar 

  16. Shickel, B., Siegel, S., Heesacker, M., Benton, S., Rashidi, P.: Automatic detection and classification of cognitive distortions in mental health text. arXiv preprint arXiv: https://arxiv.org/abs/1909.07502 (2019)

  17. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv: https://arxiv.org/abs/1412.3555 (2014)

  18. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. J. Ann. Stat. 1189–1232 (2001)

    Google Scholar 

  19. Suvorov, R., Shelmanov, A., Smirnov, I.: Active learning with adaptive density weighted sampling for information extraction from scientific papers. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds.) Artificial Intelligence and Natural Language, vol. 789, pp. 77–90. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71746-3_7

    Chapter  Google Scholar 

  20. Labeled Frustration Corpus. http://nlp.isa.ru/frustration. Accessed 24 July 2021

  21. Korobov, M.: Morphological analyzer and generator for Russian and Ukrainian languages. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds.) Analysis of Images, Social Networks and Texts, vol. 542, pp. 320–332. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26123-2_31

    Chapter  Google Scholar 

  22. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the International Conference on Machine Learning, PMLR, pp.1188–1196 (2014)

    Google Scholar 

  23. Suvorov, R., Sochenkov, I., Tikhomirov, I.: Method for pornography filtering in the web based on automatic classification and natural language processing. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) Speech and Computer, vol. 8113, pp. 233–240. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01931-4_31

    Chapter  Google Scholar 

  24. Donchenko, D., Ovchar, N., Sadovnikova, N., Parygin, D., Shabalina, O., Ather, D.: Analysis of comments of users of social networks to assess the level of social tension. Procedia Comput. Sci. 119, 359–367 (2017)

    Article  Google Scholar 

  25. Osipov, G.S., Smirnov, I.V., Tikhomirov, I.A.: Relational-situational method for text search and analysis and its applications. Sci. Tech. Inf. Process. 37(6), 432–437 (2010). https://doi.org/10.3103/S0147688210060080

    Article  Google Scholar 

  26. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  27. Benko, V., Zakharov, V.P.: Very large Russian corpora: new opportunities and new challenges. In: Computational Linguistics and Intellectual Technologies, pp. 79–93 (2016)

    Google Scholar 

  28. Larionov, D., Shelmanov, A., Chistova, E., Smirnov, I.: Semantic role labeling with pretrained language models for known and unknown predicates. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 619–628 (2019)

    Google Scholar 

  29. Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H.: XGBoost: extreme gradient boosting. R package version 0.42 1(4), 1–14 (2015)

    Google Scholar 

  30. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  31. Burtsev, M., et al.: DeepPavlov: open-source library for dialogue systems. In: Proceedings of ACL 2018, System Demonstrations, pp. 122–127 (2018)

    Google Scholar 

  32. Manning, C.D., Clark, K., Hewitt, J., Khandelwal, U., Levy, O.: Emergent linguistic structure in artificial neural networks trained by self-supervision. Proc. Natl. Acad. Sci. 117(48), 30046–30054 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

This study is supported by Russian Foundation for Basic Research, grant No. 18–29-22047 mk.

Author information

Authors and Affiliations

  1. Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, Moscow, Russia

    Dmitry Devyatkin, Natalia Chudova & Anfisa Chuganskaya

  2. Moscow Institute of Physics and Technology, Moscow, Russia

    Daria Sharypina

Authors
  1. Dmitry Devyatkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalia Chudova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anfisa Chuganskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daria Sharypina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dmitry Devyatkin .

Editor information

Editors and Affiliations

  1. Rostov State Transport University, Rostov-on-Don, Russia

    Sergei M. Kovalev

  2. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  3. Russian Academy of Sciences, Moscow, Russia

    Aleksandr I. Panov

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Devyatkin, D., Chudova, N., Chuganskaya, A., Sharypina, D. (2021). Methods for Recognition of Frustration-Derived Reactions on Social Media. In: Kovalev, S.M., Kuznetsov, S.O., Panov, A.I. (eds) Artificial Intelligence. RCAI 2021. Lecture Notes in Computer Science(), vol 12948. Springer, Cham. https://doi.org/10.1007/978-3-030-86855-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86855-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86854-3

  • Online ISBN: 978-3-030-86855-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation