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Semiotic Function of Empathy in Text Emotion Assessment

Abstract

The focus of this paper is to discuss the semiotic aspects of our findings from a project we conducted in the frame of Emotional Text Analysis paradigm. In the project, we intended to create a computer text classifier capable of effectively classifying texts into emotional categories. We agreed that we would need discrete data samples to input into it. For this, we asked 178 informants to give a verdict on the dominant emotion of 48 sample texts. Prior to their assessment of the texts, the informants responded to a questionnaire used to estimate their empathic tendency. A detailed analysis of the informants’ assessments and personal empathetic tendency scores showed a positive correlation. Subsequently, our interest was piqued by the issue of how emotions could be triggered by conventional signs (words). Our findings seem to suggest that words are only used as an expression form insofar as they embody another type of semiotic complexity, thus diverging from the traditional Pearcian triad. In order to develop on these findings, it is therefore the main objective of this paper to provide a biosemiotic model of representation/interpretation of emotions, with particular attention paid to the eliciting of emotions as sign types. In this endeavour, we draw upon K. Kull’s concept of emonic semiotic model realization. Our suggestion is that, when one processes a text that elicits an emotional response, two semiotic facets are relevant: indexicality and emonicity. We argue that it is a main empathetic function to enforce the emonic model of semiosis over the indexical in situations where the interpreter has a choice between the two. As such, the hypothesis of the study is that emotions facilitate a particular type of semiotic mechanism, relying on the mimesis principle.

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Data Availability

The data that support the findings of this study are openly available in Git Hub at https://github.com/AlexDel/levheimcube.

Code Availability

The code that supports the findings of this study is openly available https://colab.research.google.com/drive/15_0q1ff7_2fuldHoC1qzh4U7av16qM1V#scrollTo=hWZuCNQIynAI.

References

  1. Barrett, L. F. (2012). Emotions are real. Emotion, 12(3), 413–429. https://doi.org/10.1037/a0027555.

    Article  PubMed  Google Scholar 

  2. Berthoz, A. (2012). Simplexity: Simplifying Principles for a Complex World. Yale University Press.

  3. Berthoz, A., & Christen, Y. (2009). Neurobiology of “Umwelt”: How Living Beings Perceive the World. Springer.

  4. Blinov, P. D., & Kotelnikov, E. V. (2015). Semantic similarity for aspect-based sentiment analysis. Russian Digital Libraries Journal, 18(3–4), 120–137.

    Google Scholar 

  5. Boler, M. (1997). The risks of empathy: Interrogating multiculturalism’s gaze. Cultural Studies, 11(2), 253–273. https://doi.org/10.1080/09502389700490141.

    Article  Google Scholar 

  6. Carr, L., Iacoboni, M., Dubeau, M.-C., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences of the United States of America, 100(9), 5497–5502. https://doi.org/10.1073/pnas.0935845100.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Chetviorkin, I. I., & Loukachevitch, N. V. (2013). Sentiment analysis track at ROMIP-2012. Proceedings of International Conference Dialog-2013, 2, 40–50.

  8. Cuff, B. M. P., Brown, S. J., Taylor, L., & Howat, D. J. (2016). Empathy: A review of the concept. Emotion Review, 8(2), 144–153. https://doi.org/10.1177/1754073914558466.

    Article  Google Scholar 

  9. Das, S., & Chen, M. (2001). Yahoo! for Amazon: Extracting market sentiment from stock message boards. Proceedings of the Asia Pacific Finance Association Annual Conference (APFA) (pp. 1–16).

  10. Decety, J., & Jackson, P. L. (2004). The functional architecture of human empathy. Behavioral and Cognitive Neuroscience Reviews, 3(2), 71–100. https://doi.org/10.1177/1534582304267187.

    Article  Google Scholar 

  11. Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. The Scientific World Journal, 6, 1146–1163. https://doi.org/10.1100/tsw.2006.221.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Faltýnek, D., & Lacková, Ľ (2020). In the case of protosemiosis: Indexicality vs. iconicity of proteins. Biosemiotics. https://doi.org/10.1007/s12304-020-09396-7.

    Article  Google Scholar 

  13. Favareau, D., Kull, K., Ostdiek, G., Maran, T., Westling, L., Cobley, P., Stjernfelt, F., Anderson, M., Tønnessen, M., & Wheeler, W. (2017). How can the study of the humanities inform the study of biosemiotics? Biosemiotics, 10, 9–31. https://doi.org/10.1007/s12304-017-9287-6.

    Article  Google Scholar 

  14. Filimon, F., Nelson, J. D., Hagler, D. J., & Sereno, M. I. (2007). Human cortical representations for reaching: Mirror neurons for execution, observation, and imagery. NeuroImage, 37(4), 1315–1328. https://doi.org/10.1016/j.neuroimage.2007.06.008.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fuller, S. (2019). The brain as artificial intelligence: Prospecting the frontiers of neuroscience. AI & Society, 34(4), 825–833. https://doi.org/10.1007/s00146-018-0820-1.

    Article  Google Scholar 

  16. Hoffmeyer, J. (2018). Knowledge is never just there. Biosemiotics, 11, 1–5. https://doi.org/10.1007/s12304-018-9320-4.

    Article  Google Scholar 

  17. Hoffmeyer, J., & Stjernfelt, F. (2016). The great chain of semiosis. Investigating the steps in the evolution of semiotic competence. Biosemiotics, 9, 7–29. https://doi.org/10.1007/s12304-015-9247-y.

    Article  Google Scholar 

  18. Kull, K. (2019). Steps towards the natural meronomy and taxonomy of semiosis: Emotin between index and symbol? Sign Systems Studies, 47(1/2), 88–104. https://doi.org/10.12697/SSS.2019.47.1-2.03.

    Article  Google Scholar 

  19. Lemke, J. (2015). Feeling and meaning: A unitary bio-semiotic account. International Handbook of Semiotics (pp. 589–616). https://doi.org/10.1007/978-94-017-9404-6_27.

  20. Lövheim, H. (2012). A new three-dimensional model for emotions and monoamine neuro-transmitters. Medical Hypotheses, 78(2), 341–348. https://doi.org/10.1016/j.mehy.2011.11.016.

    CAS  Article  PubMed  Google Scholar 

  21. Maran, T. (2011). Becoming a sign: The mimic’s activity in biological mimicry. Biosemiotics, 4(2), 243–257. https://doi.org/10.1007/s12304-010-9095-8.

    Article  Google Scholar 

  22. Massumi, B. (1995). The autonomy of affect. Cultural Critique, 31, 83–109. https://doi.org/10.2307/1354446.

    Article  Google Scholar 

  23. Mehrabian, A., & Epstein, N. (1972). A measure of emotional empathy. Journal of Personality, 40(4), 525–543. https://doi.org/10.1111/j.1467-6494.1972.tb00078.x.

    CAS  Article  PubMed  Google Scholar 

  24. Meltzoff, A. N., & Moore, M. K. (1994). Imitation, memory, and the representations of persons. Infant Behavior & Development, 17(1), 83–99. https://doi.org/10.1016/0163-6383(94)90024-8.

    Article  Google Scholar 

  25. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. K-CAP’03: Proceedings of the 2nd International Conference on Knowledge Capture, (pp. 70–77). https://doi.org/10.1145/945645.945658.

  26. Noten, M. M. P. G., Van der Heijden, K. B., Huijbregts, S. C. J., Van Goozen, S. H. M., & Swaab, H. (2019). Indicators of affective empathy, cognitive empathy, and social attention during emotional clips in relation to aggression in 3-year-olds. Journal of Experimental Child Psychology, 185, 35–50. https://doi.org/10.1016/j.jecp.2019.04.012.

    CAS  Article  PubMed  Google Scholar 

  27. Pang, B., Lee, L., & Vaithyanathan, Sh (2002). Thumbs up? Sentiment classification using machine learning techniques. EMNLP’02: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 10, 79–86. https://doi.org/10.3115/1118693.1118704.

  28. Queiroz, J., Stjernfelt, F., & El-Hani, C. N. (2014). Dicent symbols and proto-propositions in biological mimicry. Peirce and Biosemiotics, Biosemiotics, 11, 199–213. https://doi.org/10.1007/978-94-007-7732-3_11.

    Article  Google Scholar 

  29. Rakovsky, A., Moskvichev, A., & Filchenkov, A. (2016). Data augmentation method for the image sentiment analysis. AINL FRUCT: Artificial Intelligence and Natural Language Conference proceedings (pp. 106–109).

  30. Schippers, M. B., Roebroeck, A., Renken, R., Nanetti, L., & Keysers, Ch (2010). Mapping the information flow from one brain to another during gestural communication. Proceedings of the National Academy of Sciences of the United States of America, 107(20), 9388–9393. https://doi.org/10.1073/pnas.1001791107.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ureña Gómez-Moreno, J. M. (2019). The ‘mimic’ or ‘mimetic’ octopus? A cognitive-semiotic study of mimicry and deception in thaumoctopus mimicus. Biosemiotics, 12(3), 441–467. https://doi.org/10.1007/s12304-019-09362-y.

    Article  Google Scholar 

  32. Vasilyuk, F. E. (2016). Semiotics and the technique of empathy. Journal of Russian & East European Psychology, 53(2), 56–79. https://doi.org/10.1080/10610405.2016.1230994.

    Article  Google Scholar 

  33. Verhofstadt, L., Devoldre, I., Buysse, A., Stevens, M., Hinnekens, C., Ickes, W., & Davis, M. (2016). The role of cognitive and affective empathy in spouses’ support interactions: An observational study. PLoS One, 11(2). https://doi.org/10.1371/journal.pone.0149944.

  34. von Uexküll, J. (1921). Umwelt und Innenwelt der Tiere. Springer.

  35. von Uexküll, J. (1957). A stroll through the worlds of animals and men: a picture book of invisible worlds. In C. H. Schiller (Ed.), Instinctive Behavior: The Development of a Modern Concept (pp. 5–80). International Universities Press.

  36. von Uexküll, J. (2009). The theory of meaning. Semiotica, 42(1), 25–82. https://doi.org/10.1515/semi.1982.42.1.25.

    Article  Google Scholar 

  37. von Uexküll, J., Müller, J., & von Uexküll, T. (1977). Der Sinn des Lebens. Ernst Klett Verlag.

  38. Yalcin, Ó¦N., & DiPaola, S. (2018). A computational model of empathy for interactive agents. Biologically Inspired Cognitive Architectures, 26, 20–25. https://doi.org/10.1016/j.bica.2018.07.010.

    Article  Google Scholar 

  39. Yu, Ch-L., & Chou, T.-L. (2018). A dual route model of empathy: A neurobiological prospective. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.02212.

  40. Zlatev, J. (2007). Intersubjectivity, mimetic schemas and the emergence of language. Intellectica. Revue de l’Association pour la Recherche Cognitive, 2–3(46–48), 123–151. https://doi.org/10.3406/intel.2007.1281.

    Article  Google Scholar 

  41. Zlatev, J. (2009). The semiotic hierarchy: Life, consciousness, signs and language. Cognitive Semiotics, 4, 169–200. https://doi.org/10.1515/cogsem.2009.4.spring2009.169.

    Article  Google Scholar 

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Acknowledgements

We want to thank the Russian Foundation for Basic Research for supporting our research (project № 19-012-00205 “Design of Sentiment Classifier for Internet Texts in Russian backed by Lövheim’s Cube Emotion Model”). An early version of this paper was presented at the conference “19th Annual Gathering in Biosemiotics” (Moscow State University, July 1–5th 2019). We are thankful for the feedback we received at the conference.

Funding

The research leading to these results received funding from the Russian Foundation for Basic Research, Grant № 19-012-00205 “Design of Sentiment Classifier for Internet Texts in Russian backed by Lövheim’s Cube Emotion Model”.

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Correspondence to Anastasia Kolmogorova.

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Kolmogorova, A., Kalinin, A. & Malikova, A. Semiotic Function of Empathy in Text Emotion Assessment. Biosemiotics (2021). https://doi.org/10.1007/s12304-021-09434-y

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Keywords

  • Emotion assessment magnitude
  • Empathy
  • Mirror neurons
  • Sentiment analysis
  • Sign typology
  • Umwelt