Metaphor Analytics: A neural network approach to automated identification of metaphorical speech impact
Metaphors are powerful tools in discourse, shaping how we interpret events, issues, or concepts. Consequently, developing methods for automated and comprehensive analysis of metaphors in large-scale textual data becomes a critical task. In this context, the ability of artificial intelligence to generate, interpret and relate metaphors to the cognitive-pragmatic context of speech becomes a particularly relevant question. Modern AI tools are predominantly developed by Western research teams. They rely on foreign technologies and are based on the methodology of foreign cognitive linguistics. They are also developed using English-language corpora. As a result, they depend on foreign technology stacks, they are methodologically rooted in Western cognitive linguistics and trained almost exclusively on English-language corpora. This study therefore aims to provide a solution for identifying and analyzing metaphors in Russian-language texts by developing a novel tool methodologically grounded in Russian linguistics and built using domestic technological resources. The approach draws on the theory of metaphorical speech impact. Within this theory, the functional potential of any metaphor in discourse unfolds through four functions (representational, evaluative, persuasive, and suggestive) across cognitive, semantic, and communicative levels and is subsequently quantified through specific metaphoricity indices. Technically, the solution employs a hybrid approach, combining prompt engineering, rule-based code, and access to the YandexGPT generative model through its cloud API within a Python 3.10+ environment. The methodological procedure comprises three sequential stages from model training to metaphor analysis. In the first stage, metaphor detection and modeling, the model learns to identify metaphors, classify them by source and target domains, and construct “A is B” metaphorical models. Building on this, the second stage, metaphor classification and assessment, involves identifying the specific metaphor type (ontological, orientational, structural), determining its intensity (conventional, moderate, novel), and assessing its evaluation (negative, positive, neutral). The third and final stage is quantitative analysis and interpretation. This involves calculating the core indices of the impact of metaphorical speech – density, intensity and typology indices – and providing a comprehensive interpretation of the results. This research culminates in the development of the Metaphor Analytics Software, capable of automated metaphor detection, classification, and analysis. This program effectively fills a significant gap in the system of NLP tools for analyzing Russian-language metaphors.
Kalinin, O. I., Solopova, O. A., Solopov, A. D. (2026). Metaphor Analytics: A neural network approach to automated identification of metaphorical speech impact, Research Result. Theoretical and Applied Linguistics, 12 (2), 111–138.
While nobody left any comments to this publication.
You can be first.
Baranov, A. N. (2014). Deskriptornaya teoriya metafory [Descriptor Theory of Metaphor], Yazyki slavyanskoy kultury, Moscow, Russia. (In Russian)
Kalinin, O. I. (2022). “Metaphorical speech impact”, Cognitive Studies of Language, 51(4), 229-234. (In Russian)
Kalinin, O. I. (2023). “Metaphorical speech impact in the media discourse of the trade war between China and the USA”, Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriya: Lingvistika i mezhkulturnaya kommunikatsiya, 21(4), 70-84. https://doi.org/10.25205/1818-7935-2023-21-4-70-84(In Russian)
Kalinin, O. I. (2025). “Opportunities of neural network technologies in metaphor research”, Cognitive Studies of Language, 62 (1-2), 437-441. (In Russian)
Kalinin, O. I., Solopova, O. A. and Koshkarova, N. N. (2025). Metafora i diskurs: metodologiya lingvisticheskikh issledovaniy [Metaphor and Discourse: Methodology of Linguistic Research], VKN Publishing House, Moscow, Russia. (In Russian)
Savchuk, S. O., Arkhangelskiy, T. A., Bonch-Osmolovskaya, A. A., Donina, O. V., Kuznetsova, Yu. N., Lyashevskaya, O. N., Orekhov, B. V. and Podryadchikova, M. V. (2024). Russian National Corpus 2.0: New opportunities and development prospects, Voprosy yazykoznaniya, 2, 7-34. https://doi.org/10.31857/0373-658X.2024.2.7-34(In Russian)
Solopova, O. A. (2020). A key to the future: predictive meanings of political metaphor (based on British texts about Russia of the Great Patriotic War period), Vestnik Tomskogo gosudarstvennogo universiteta. Filologiya, 63, 161-177. https://doi.org/10.17223/19986645/63/9(In Russian)
Solopova, O. A. and Koshkarova, N. N. (2021). Religion as a source domain of metaphors in World War II media discourse, Nauchnyy dialog, 4, 148-167. (In Russian)
Solopova, O. A. and Koshkarova, N. N. (2025). Microstructure of the Multilingual Dictionary “BRICS: Discourse Metaphors”, Voprosy leksikografii, 36, 26-44. https://doi.org/10.17223/22274200/36/2(In Russian)
Chudinov, A. P. (2001). Rossiya v metaforicheskom zerkale: kognitivnoe issledovanie politicheskoy metafory (1991–2000) [Russia in the Metaphorical Mirror: A Cognitive Study of Political Metaphor], Ural State Pedagogical University Press, Yekaterinburg, Russia. (In Russian)
Chudinov, A. P., Budaev, E. V. and Solopova, O. A. (2023). Zhengzhi yin yuxue: renzhi-huayu yanjiu [Political Metaphorology: Cognitive-Discursive Studies], Peking University Press, Beijing, China. (In Russian)
Al-Tashi, Q. and Hasan, A. M. (2019). Word Sense Disambiguation: A Review. [Online], available at: https://osf.io/dbkzm/ (Accessed: 18 January 2020). (In English)
Birke, J. and Sarkar, A. (2007). Active learning for the identification of nonliteral language, Proceedings of the Workshop on Computational Approaches to Figurative Language, Rochester, NY, USA, 21–28. (In English)
Choi, M., Lee, S., Choi, E., Park, H., Lee, J., Lee, D. and Lee, J. (2021). MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Online: Association for Computational Linguistics, 1763-1773. https://doi.org/10.18653/v1/2021.naacl-main.141(In English)
Charteris-Black, J. (2016). Politicians and Rhetoric: The Persuasive Power of Metaphor, Palgrave Macmillan, London, UK. (In English)
Cirstea, B.-I. and Chiru, C.-G. (2013). Metaphor detection, Proceedings of the 19th International Conference on Control Systems and Computer Science, NW Washington, DC, USA, 210-217. https://doi.org/10.1109/CSCS.2013.74(In English)
Comșa, I., Eisenschlos, J. and Narayanan, S. (2022). MiQA: A benchmark for inference on metaphorical questions, Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Vol. 2: Short Papers), online, 373–381. (In English)
Dankers, V., Rei, M., Lewis, M. and Shutova, E. (2019). Modelling the interplay of metaphor and emotion through multitask learning, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2218-2229. https://doi.org/10.18653/v1/D19-1227(In English)
Esbrí-Blasco, M. (2024). Conceptualización metafórica de la IA en el discurso digital methaodos, Revista de Ciencias Sociales, 12(2), m241202a07. https://doi.org/10.17502/mrcs.v12i2.824(In Spanish)
Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters, Psychological Bulletin, 76 (5), 378–382. https://doi.org/10.1037/H0031619 (In English)
Fillmore, C. J. (1982). Frame Semantics, in the Linguistic Society of Korea (ed.), Linguistics in the Morning Calm, Hanshin Publishing Co, Seoul, Korea, 111-137. (In English)
Florou, E., Perifanos, K. and Goutsos, D. (2018). Neural embeddings for metaphor detection in a corpus of Greek texts, 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), Zakynthos, Greece, 1–4. (In English)
Gao, G., Choi, E., Choi, Y. and Zettlemoyer, L. (2018). Neural metaphor detection in context, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 3744-3753. https://doi.org/10.18653/v1/D18-1060(In English)
Gedigian, M., Bryant, J., Narayanan, S. and Ciric, B. (2006). Catching metaphors, Proceedings of the Third Workshop on Scalable Natural Language Understanding, New York City, NY, USA, 41-48. (In English)
Hashimoto, C. and Kawahara, D. (2009). Compilation of an idiom example database for supervised idiom identification, Language Resources and Evaluation, 43(4), 355-384. https://doi.org/10.1007/s10579-009-9104-1(In English)
Jurafsky, D. and Martin, J. H. (2025). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models [Online], available at: https://web.stanford.edu/~jurafsky/slp3 (Accessed 12 December 2025). (In English)
Kalinin, O. I. and Ignatenko, A. V. (2024). Metaphor power in the context of the author’s opinion expression and perception, Russian Journal of Linguistics, 28 (1), 166–189. https://doi.org/10.22363/2687-0088-34791(In English)
Köper, M. and Schulte im Walde, S. (2017). Improving verb metaphor detection by propagating abstractness to words, phrases and individual senses, Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, Valencia, Spain, 24–30. (In English)
Krishnakumaran, S. and Zhu, X. (2007). Hunting elusive metaphors using lexical resources, Proceedings of the Workshop on Computational Approaches to Figurative Language, Rochester, NY, USA, 13–20. (In English)
Lakoff, G. and Johnson, M. (1980). Metaphors We Live By. University of Chicago Press, Chicago, IL. (In English)
Landis, J. R. and Koch, G. G. (197). The measurement of observer agreement for categorical data, Biometrics, 33 (1), 159–174. https://doi.org/10.2307/2529310(In English)
Landtsheer, Ch. de. (2009). Collecting political meaning from the count of metaphor, in Musolff, A. and Zinken, J. (eds.), Metaphor and Discourse, Palgrave Macmillan, London, UK, 59-78. (In English)
Leong, C. W., Beigman Klebanov, B. and Shutova, E. (2018a). A Report on the 2018 VUA metaphor detection shared task, Proceedings of the Workshop on Figurative Language Processing, New Orleans, Louisiana, 56-66. https://doi.org/10.18653/v1/W18-0907(In English)
Leong, C. W., Beigman Klebanov, B. and Shutova, E. (2018b). A comparison of features for the automatic classification of literal and metaphorical uses of German particle verbs, Proceedings of the Workshop on Figurative Language Processing, New Orleans, Louisiana, 71–76. (In English)
Leong, C. W., Beigman Klebanov, B., Hamill, C., Stemle, E., Ubale, R. and Chen, X. (2020). A report on the 2020 VUA and TOEFL metaphor detection shared task, Proceedings of the Second Workshop on Figurative Language Processing, online, 18–29. (In English)
Levchenko, O., Romanyshyn, N. and Dosyn, D. (2019). Method of automated identification of metaphoric meaning in adjective + noun word combinations (based on the Ukrainian language), Workshop Proceedings of the 8th International Conference on “Mathematics. Information Technologies. Education”, MoMLeT&DS-2019, Shatsk, Ukraine, 370–380. (In English)
Martin, J. H. (1990). A Computational Model of Metaphor Interpretation. Academic Press Professional Inc., London, UK. (In English)
Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. et al. (2013). Metaphor identification in large texts corpora, PLOS ONE, 8 (4), e62343. https://doi.org/10.1371/journal.pone.0062343(In English)
Powers, D. M. W. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2 (1), 37–63. (In English)
Shutova, E., Sun, L. and Korhonen, A. (2010). Metaphor identification using verb and noun clustering, Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Beijing, China, 1002–1010. (In English)
Shutova, E., Teufel, S. and Korhonen, A. (2013). Statistical metaphor processing, Computational Linguistics, 39(2), 301-353. https://doi.org/10.1162/COLI_a_00124(In English)
Shutova, E., Reichart, R. and Korhonen, A. (2017). Neural metaphor detection in context, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 527–536. (In English)
Skrynnikova, I. V. (2024). Interpreting metaphorical language: A challenge to artificial intelligence, Science Journal of Volgograd State University. Linguistics, 23 (5), 99–107. https://doi.org/10.15688/jvolsu2.2024.5.8(In English)
Smith, A. and Klein, D. (2023). Interpreting metaphorical language: A challenge to artificial intelligence, AI & Society, 38 (4), 621–635. (In English)
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A. A. and Pasma, T. A. (2010). Method for Linguistic Metaphor Identification: From MIP to MIPVU, John Benjamins, Amsterdam. (In English)
Steen, G. J. (2017). Deliberate metaphor theory: Basic assumptions, main tenets, urgent issues, Intercultural Pragmatics, 14(1), 1-24. https://doi.org/10.1515/ip-2017-0001(In English)
Torres Rivera, L., Oliver, A. and Coll-Florit, M. (2023). Metaphorical cognition in ChatGPT and humans, Cognitive Linguistic Studies, 10 (3), 245–266. (In English)
Turney, P., Neuman, Y., Assaf, D. and Cohen, Y. (2011). Literal and Metaphorical Sense Identification through Concrete and Abstract Context, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, 680–690. (In English)
Veale, T. and Hao, Y. (2008). A Fluid knowledge representation for understanding and generating creative metaphors, Proceedings of the 22nd International Conference on Computational Linguistics (COLING), Manchester, UK, 945–952. (In English)
Zibin, A. and Solopova, O. A. (2024). Metaphors across languages, cultures, and discourses: A research agenda, Russian Journal of Linguistics, 28 (1), 7–32. https://doi.org/10.22363/2687-0088-37837(In English)
Zinken, J., Hellsten, I. and Nerlich, B. (2008). Discourse metaphors, in Dirven, R., Frank, R., Ziemke, T. and Zlatev, J. (eds.), Body, Language, and Mind. Vol. 2: Sociocultural Situatedness Mouton, Berlin, 363–385. (In English)
Corpus Material
Russian National Corpus [Online], available at: https://ruscorpora.ru (accessed 8 April 2025). (In Russian)
Certificate of state registration of the Metaphor AnalyticsSoftware, No. 2025693079 (Rospatent). (In Russian)
The study is funded by the Russian Science Foundation, Project No. 24-18-00049 “Modeling the image of Russia in BRICS’ media discourses: frames, metaphors, and stereotypes”.