Журнал «Информационные технологии и вычислительные системы» - I. V. Smirnov Software for Psycho)Emotional Text Processing

The paper considers the problem of psycho-emotional text processing, aimed at identifying the psychological characteristics of the author of the text and identifying the emotional characteristics of the text based on methods of psycholinguistics and artificial intelligence. A tool for psychoemotional analysis of texts in Russian is described as well as application of the tool to analysis of the VKontakte users’ reaction to fake messages is presented.

1. Enikolopov, S.N., Medvedeva, T.I., Vorontcova, O.Y. 2019. Ocenka tekstov, napisannyh bol'nymi endogennymi psihicheskimi zabolevaniyami [Evaluation of texts written by patients with endogenous mental illnesses]. Psihiatriya [Psychiatry]. 81:56–64.

2. Pennebaker J. W., Francis M. E., Booth R. J. Linguistic inquiry and word count: LIWC 2001 // Mahway: Lawrence Erlbaum Associates. – 2001. – V. 71. – №. 2001. – p. 2001.

3. Štajner S., Yenikent S. A survey of automatic personality detection from texts // Proceedings of the 28th International Conference on Computational Linguistics. – 2020. –pp. 6284-6295.

4. Ahmad H., Asghar M. Z., Khan A. S., Habib A. A systematic literature review of personality trait classification from textual content // Open Computer Science. – 2020. – V. 10. – №. 1. – pp. 175-193.

5. YArushkina, N. G., Moshkin, V. S., Andreev, I. A. 2022. Algoritm psiholingvisticheskogo analiza tekstovyh dannyh social'nyh setej s primeneniem modeli «Bol'shaya pyatyorka» [Algorithm of psycholinguistic analysis of text data of social networks using the "Big Five" model]. Ontologiya proektirovaniya [Design Ontology] 1(43):82-92.

6. Acheampong F. A., Wenyu C., Nunoo‐Mensah H. Textbased emotion detection: Advances, challenges, and opportunities // Engineering Reports. – 2020. – V. 2. – №. 7. – p. e12189.

7. Nandwani P., Verma R. A review on sentiment analysis and emotion detection from text // Social Network Analysis and Mining. – 2021. – V. 11. – №. 1. – pp. 1-19.

8. Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, and Marcos Zampieri. Benchmarking Aggression Identification in Social Media // Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018). – 2018. – pp. 1-11. Association for Computational Linguistics.

9. Faneva Ramiandrisoa, Josiane Mothe. Aggression Identification in Social Media: a Transfer Learning Based Approach // Second Workshop on Trolling, Aggression and Cyberbullying, European Language Resources Association (ELRA), May 2020, Marseille, France. pp.26-31.

10. Kolmogorova, A.V., Vdovina, L.A. 2019. Leksikogrammaticheskie markery emocij kak parametry dlya sentiment analiza russkoyazychnyh internet-tekstov [Lexicogrammatical markers of emotions as parameters for sentimental analysis of Russian-language Internet texts]. Vestnik Permskogo universiteta. Rossijskaya i zarubezhnaya filologiya [Bulletin of Perm University. Russian and foreign philology]. 11(3):38-46.

11. Kolmogorova, A.V., Kalinin ,A.A. 2022 Emocional'nyj analiz postov VKontakte: klassifikator ili regressor? [Emotional analysis of VKontakte posts: classifier or regressor?]. Komp'yuternaya lingvistika i intellektual'nye tekhnologii: Po materialam ezhegodnoj mezhdunarodnoj konferencii «Dialog». Moskva [Computational linguistics and intelligent technologies: Based on the materials of the annual international conference "Dialogue". Moscow] 21:311-322.

12. Ghazi D., Inkpen D., Szpakowicz S. Detecting emotion stimuli in emotion-bearing sentences // International Conference on Intelligent Text Processing and Computational Linguistics. – Springer, Cham. – 2015. – pp. 152-165.

13. Campagnano C., Conia S., Navigli R. SRL4E–Semantic Role Labeling for Emotions: A Unified Evaluation Framework // Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). – 2022. – pp. 4586-4601

14. Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. Natural language processing in mental health applications using non-clinical texts // Natural Language Engineering. – 2017. – V. 23. – №. 5. – pp. 649-685.

15. Kayalvizhi S. et al. Findings of the Shared Task on Detecting Signs of Depression from Social Media // Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. – 2022. – pp. 331-338.

16. M. Stankevich, I. Smirnov, Y. Kuznetsova, N. Kiselnikova, S. Enikolopov. Predicting Depression from Essays in Russian // In Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2019”. – 2019. – pp. 637–647.

17. Mathur, P. Kubde and S. Vaidya, "Emotional Analysis using Twitter Data during Pandemic Situation: COVID-19 // 5th International Conference on Communication and Electronics Systems (ICCES). – 2020. – pp. 845-848.

18. Becker K., Harb J. G., Ebeling R. Exploring deep learning for the analysis of emotional reactions to terrorist events on twitter // Journal of Information and Data Management. – 2019. – V. 10. – №. 2. – pp. 97-115.

19. Harb J. G. D., Ebeling R., Becker K. A framework to analyze the emotional reactions to mass violent events on Twitter and influential factors // Information Processing & Management. – 2020. – V. 57. – №. 6. – p. 102372.

20. Enikolopov, S.N., Kuznecova, YU.M., Osipov, G.S., Smirnov, I.V., Chudova, N.V. 2021. Metod relyacionnosituacionnogo analiza teksta v psihologicheskih issledovaniyah [The method of relational-situational text analysis in psychological research]. Psihologiya. ZHurnal Vysshej shkoly ekonomiki [Psychology. Journal of the Higher School of Economics] 18(4):748-769.

21. Smirnov I., Stankevich M., Kuznetsova Y., Suvorova M., Larionov D., Nikitina E., Savelov M., Grigoriev O. TITANIS: A Tool for Intelligent Text Analysis in Social Media // In: Kovalev S.M., Kuznetsov S.O., Panov A.I. (eds) Artificial Intelligence. RCAI 2021. Lecture Notes in Computer Science, Springer, Cham. – 2021. – V. 12948. – pp.232-247.

22. Enikolopov, S.N, Kuznecova, YU.M., Minin, A.N., Penkina, M.YU., Smirnov, I.V., Stankevich, M.A., Chudova, N.V. 2019. Osobennosti teksta i psihologicheskie osobennosti: opyt empiricheskogo komp'yuternogo issledovaniya [Text features and psychological features: the experience of empirical computer research]. Trudy Instituta Sistemnogo Analiza Rossijskoj akademii nauk [Proceedings of the Institute for System Analysis of the Russian Academy of Sciences] 69(3):91-99.

23. Enikolopov, S.N., Kovalev, A.K., Kuznecova, YU.M., Starostina, E.N., Chudova, N.V. 2019. Priznaki, harakternye dlya pis'mennyh tekstov, napisannyh v sostoyanii frustracii [Characteristic of texts written in a state of frustration]. Vestnik MGU. Seriya 14. Psihologiya [Bulletin of Moscow State University. Series 14. Psychology] 3:66-85.

24. Shelmanov A. O., Smirnov I. V., Methods for Semantic Role Labeling of Russian Texts // Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference "Dialogue" (2014). Issue 13 (20). – 2014. – pp. 580-592.

25. Stankevich M., Latyshev A., Kuminskaya E., Smirnov I., Grigoriev O. Depression Detection from Social Media Texts // Proceedings of Data Analytics and Management in Data Intensive Domains: ХХI International Conference DAМDID/RCDL'2019 – 2019. – pp. 352-362.

26. Devyatkin, Dmitry A., Natalia V. Chudova, Anfisa A. Chuganskaya and Daria Sharypina. 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, Springer, Cham. – V. 12948. – pp. 17-30.

27. Chistova E., Shelmanov A., Pisarevskaya D., Kobozeva M., Isakov V., Panchenko A., Toldova S., Smirnov I. RST Discourse Parser for Russian: an Experimental Study of Deep Learning Models //International Conference on Analysis of Images, Social Networks and Texts. – Lecture Notes in Computer Science, Springer, Cham. – 2021. – V.12602. – pp.105-119.