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Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 41

Psychological Needs as Credible Song Signals: Testing Large Language Models to Annotate Lyrics

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DOI: http://dx.doi.org/10.15439/2024F7168

Citation: Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 41, pages 159–168 ()

Full text

Abstract. Our preliminary study presents a new perspective in music information retrieval by investigating how contemporary song-making and listening emulate our innate responses, similar to the primal vocalizations of primates, drawing from music's origins as credible signaling. The diversity of musical expressions within a single culture suggests that it arises from group dynamics reflecting individuals' psychological fitness. Derived from the temporal need-threat framework of ostracism---an evolutionarily stable strategy to influence individuals in a group, we argue that individual differences in song-making and listening can be reduced to songs' lyrical expressions in terms of four basic psychological needs: self-esteem, self-control, seeking to belong, and seeking recognition. We propose a four-binary-decision model to classify English song lyrics for hierarchically organizing the variations of musical expressions. Annotating 260 English song lyrics using ChatGPT-4s with human validation and fine-tuning GPT-3.5-turbo to develop an automated classifier have identified some limitations in current large language models.

References

  1. T. DeNora, Music asylums: Wellbeing through music in everyday life. Routledge, 2016.
  2. W. M. Randall and N. S. Rickard, “Reasons for personal music listening: A mobile experience sampling study of emotional outcomes,” Psychology of Music, vol. 45, no. 4, pp. 479–495, 2017. http://dx.doi.org/10.1177/0305735616666939
  3. P. Rowe and B. Guerin, “Contextualizing the mental health of metal youth: A community for social protection, identity, and musical empowerment,” Journal of Community Psychology, vol. 46, no. 4, pp. 429–441, 2018. http://dx.doi.org/10.1002/jcop.21949
  4. T. ter Bogt, N. Canale, M. Lenzi, A. Vieno, and R. van den Eijnden, “Sad music depresses sad adolescents: A listener’s profile,” Psychology of Music, vol. 49, no. 2, pp. 257–272, 2021. http://dx.doi.org/10.1177/0305735619849622
  5. K. D. Williams, “Ostracism,” Annu. Rev. Psychol., vol. 58, pp. 425–452, 2007. http://dx.doi.org/10.1146/annurev.psych.58.110405.085641
  6. K. D. Williams, Ostracism: The power of silence. Guilford Press, 2002.
  7. E. D. Wesselmann, M. R. Grzybowski, D. M. Steakley-Freeman, E. R. DeSouza, J. B. Nezlek, and K. D. Williams, “Social exclusion in everyday life,” Social exclusion: Psychological approaches to understanding and reducing its impact, pp. 3–23, 2016. http://dx.doi.org/10.1007/978-3-319-33033-4_1
  8. C. M. Büttner, M. Jauch, M. Marinucci, K. D. Williams, R. Greifeneder, P. Riva, and S. C. Rudert, “It will (never) stop hurting: Do repeated or chronic experiences of exclusion lead to hyper-or hyposensitive psychological responses?” Group Processes & Intergroup Relations, p. 13684302221140002, 2023. http://dx.doi.org/10.1177/13684302221140002
  9. G. A. Dingle, J. Hodges, and A. Kunde, “Tuned in emotion regulation program using music listening: Effectiveness for adolescents in educational settings,” Frontiers in psychology, vol. 7, p. 859, 2016. http://dx.doi.org/10.3389/fpsyg.2016.00859
  10. S. Uhlig, E. Jansen, and E. Scherder, ““being a bully isn’t very cool. . . “: Rap & sing music therapy for enhanced emotional self-regulation in an adolescent school setting–a randomized controlled trial,” Psychology of music, vol. 46, no. 4, pp. 568–587, 2018. http://dx.doi.org/10.1177/0305735617719154
  11. S. Garrido, M. Du Toit, and T. Meade, “Music listening and emotion regulation: Young people’s perspectives on strategies, outcomes, and intervening factors.” Psychomusicology: Music, Mind, and Brain, 2022. http://dx.doi.org/10.1037/pmu0000285
  12. E. Michels-Ratliff and M. Ennis, “This is your song: Using participants’ music selections to evoke nostalgia and autobiographical memories efficiently.” Psychomusicology: Music, Mind, and Brain, vol. 26, no. 4, p. 379, 2016. http://dx.doi.org/10.1037/pmu0000167
  13. S. Laiho, “The psychological functions of music in adolescence,” Nordic Journal of music therapy, vol. 13, no. 1, pp. 47–63, 2004. http://dx.doi.org/10.1080/08098130409478097
  14. G. Kreutz, “Does singing facilitate social bonding?” Music and Medicine, vol. 6, no. 2, pp. 51–60, 2014. http://dx.doi.org/10.47513/mmd.v6i2.180
  15. C. N. DeWall, R. S. Pond Jr, W. K. Campbell, and J. M. Twenge, “Tuning in to psychological change: Linguistic markers of psychological traits and emotions over time in popular us song lyrics.” Psychology of Aesthetics, Creativity, and the Arts, vol. 5, no. 3, p. 200, 2011. http://dx.doi.org/10.1037/a0023195
  16. T. Schäfer, P. Sedlmeier, C. Städtler, and D. Huron, “The psychological functions of music listening,” Frontiers in psychology, vol. 4, p. 511, 2013. http://dx.doi.org/10.3389/fpsyg.2013.00511
  17. K. D. Williams, “Ostracism: A temporal need-threat model,” Advances in experimental social psychology, vol. 41, pp. 275–314, 2009. http://dx.doi.org/10.1016/S0065-2601(08)00406-1
  18. C. H. Hartgerink, I. Van Beest, J. M. Wicherts, and K. D. Williams, “The ordinal effects of ostracism: A meta-analysis of 120 cyberball studies,” PloS one, vol. 10, no. 5, p. e0127002, 2015. http://dx.doi.org/10.1371/journal.pone.0127002
  19. S. A. Mehr, M. M. Krasnow, G. A. Bryant, and E. H. Hagen, “Toward a productive evolutionary understanding of music,” The Behavioral and brain sciences, vol. 44, p. e122, 2021. http://dx.doi.org/10.1017/s0140525x21000030
  20. S. A. Mehr, M. M. Krasnow, G. A. Bryant, and E. H. Hagen, “Origins of music in credible signaling,” Behavioral and Brain Sciences, vol. 44, p. e60, 2021. http://dx.doi.org/10.1017/S0140525X20000345
  21. W. contributors, “Signalling theory,” 2023, accessed: 2024-05-20. [Online]. Available: https://en.wikipedia.org/wiki/Signalling_theory
  22. P. N. Juslin and D. Västfjäll, “Emotional responses to music: The need to consider underlying mechanisms,” Behavioral and brain sciences, vol. 31, no. 5, pp. 559–575, 2008. http://dx.doi.org/10.1017/s0140525x08005293
  23. T. Eerola and J. K. Vuoskoski, “A review of music and emotion studies: Approaches, emotion models, and stimuli,” Music Perception: An Interdisciplinary Journal, vol. 30, no. 3, pp. 307–340, 2012. http://dx.doi.org/10.1525/mp.2012.30.3.307
  24. P. Ekman, “An argument for basic emotions,” Cognition & Emotion, vol. 6, no. 3-4, pp. 169–200, 1992. http://dx.doi.org/10.1080/02699939208411068
  25. C. E. Izard and C. E. Izard, “Differential emotions theory,” Human emotions, pp. 43–66, 1977. http://dx.doi.org/10.1007/978-1-4899-2209-0_3
  26. R. E. Thayer, J. R. Newman, and T. M. McClain, “Self-regulation of mood: strategies for changing a bad mood, raising energy, and reducing tension.” Journal of personality and social psychology, vol. 67, no. 5, p. 910, 1994. http://dx.doi.org/10.1037//0022-3514.67.5.910
  27. J. A. Russell, “A circumplex model of affect.” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980. http://dx.doi.org/10.1037/h0077714
  28. R. Plutchik, Emotions and life: Perspectives from psychology, biology, and evolution. American Psychological Association, 2003.
  29. D. Keltner, J. A. Brooks, and A. Cowen, “Semantic space theory: Data-driven insights into basic emotions,” Current Directions in Psychological Science, p. 09637214221150511, 2023. http://dx.doi.org/10.1177/09637214221150511
  30. C. E. Izard, “Emotion theory and research: Highlights, unanswered questions, and emerging issues,” Annual review of psychology, vol. 60, pp. 1–25, 2009. http://dx.doi.org/10.1146/annurev.psych.60.110707.163539
  31. Y. Wang, W. Song, W. Tao, A. Liotta, D. Yang, X. Li, S. Gao, Y. Sun, W. Ge, W. Zhang et al., “A systematic review on affective computing: Emotion models, databases, and recent advances,” Information Fusion, vol. 83, pp. 19–52, 2022. http://dx.doi.org/10.48550/arXiv.2203.06935
  32. V. Chaturvedi, A. B. Kaur, V. Varshney, A. Garg, G. S. Chhabra, and M. Kumar, “Music mood and human emotion recognition based on physiological signals: a systematic review,” Multimedia Systems, vol. 28, no. 1, pp. 21–44, 2022. http://dx.doi.org/10.1007/s00530-021-00786-6
  33. A. Name, “Spotify patented emotional recognition technology to recommend songs based on users’ emotions,” 2022, accessed: 2023-10-16. [Online]. Available: https://jolt.richmond.edu/2022/01/11/spotify-patented-emotional-recognition-technology-to-recommend-\protect\penalty\z@songs-based-on-users-emotions/
  34. S. Koelsch, E. Kasper, D. Sammler, K. Schulze, T. Gunter, and A. D. Friederici, “Music, language and meaning: brain signatures of semantic processing,” Nature neuroscience, vol. 7, no. 3, pp. 302–307, 2004. http://dx.doi.org/https://doi.org/10.1038/nn1197
  35. T. Schäfer, P. Sedlmeier, C. Städtler, and D. Huron, “The psychological functions of music listening,” Frontiers in psychology, vol. 4, p. 511, 2013. http://dx.doi.org/10.3389/fpsyg.2013.00511
  36. K. Schäfer and T. Eerola, “How listening to music and engagement with other media provide a sense of belonging: An exploratory study of social surrogacy,” Psychology of Music, vol. 48, no. 2, pp. 232–251, 2020. http://dx.doi.org/10.1177/0305735618795036
  37. R. Granot, D. H. Spitz, B. R. Cherki, P. Loui, R. Timmers, R. S. Schaefer, J. K. Vuoskoski, R.-N. Cárdenas-Soler, J. F. Soares-Quadros Jr, S. Li et al., ““help! i need somebody”: music as a global resource for obtaining wellbeing goals in times of crisis,” Frontiers in Psychology, vol. 12, p. 648013, 2021. http://dx.doi.org/10.3389/fpsyg.2021.648013
  38. I. Molnar-Szakacs and K. Overy, “Music and mirror neurons: from motion to’e’motion,” Social cognitive and affective neuroscience, vol. 1, no. 3, pp. 235–241, 2006. http://dx.doi.org/10.1093/scan/nsl029
  39. L. Fogassi, P. F. Ferrari, B. Gesierich, S. Rozzi, F. Chersi, and G. Rizzolatti, “Parietal lobe: from action organization to intention understanding,” Science, vol. 308, no. 5722, pp. 662–667, 2005. http://dx.doi.org/10.1126/science.1106138
  40. L. Carr, M. Iacoboni, M.-C. Dubeau, J. C. Mazziotta, and G. L. Lenzi, “Neural mechanisms of empathy in humans: a relay from neural systems for imitation to limbic areas,” Proceedings of the national Academy of Sciences, vol. 100, no. 9, pp. 5497–5502, 2003. http://dx.doi.org/10.1073/pnas.0935845100
  41. A. M. Liberman and D. H. Whalen, “On the relation of speech to language,” Trends in cognitive sciences, vol. 4, no. 5, pp. 187–196, 2000. http://dx.doi.org/https://doi.org/10.1016/s1364-6613(00)01471-6
  42. C. Y. Wan, K. Demaine, L. Zipse, A. Norton, and G. Schlaug, “From music making to speaking: Engaging the mirror neuron system in autism,” Brain research bulletin, vol. 82, no. 3-4, pp. 161–168, 2010. http://dx.doi.org/10.1016/j.brainresbull.2010.04.010
  43. G. L. Wagener, M. Berning, A. P. Costa, G. Steffgen, and A. Melzer, “Effects of emotional music on facial emotion recognition in children with autism spectrum disorder (asd),” Journal of Autism and Developmental Disorders, vol. 51, pp. 3256–3265, 2021. http://dx.doi.org/10.1007/s10803-020-04781-0
  44. R. Plutchik, “A psychoevolutionary theory of emotions,” 1982.
  45. J. P. Hewitt, “22 the social construction of self-esteem,” The Oxford handbook of positive psychology, p. 309, 2020. http://dx.doi.org/10.1093/oxfordhb/9780195187243.013.0020
  46. D. Ren, E. Wesselmann, and K. D. Williams, “Evidence for another response to ostracism: Solitude seeking,” Social Psychological and Personality Science, vol. 7, no. 3, pp. 204–212, 2016. http://dx.doi.org/10.1177/1948550615616169
  47. S. E. Taylor, L. C. Klein, B. P. Lewis, T. L. Gruenewald, R. A. Gurung, and J. A. Updegraff, “Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight.” Psychological review, vol. 107, no. 3, p. 411, 2000. http://dx.doi.org/10.1037/0033-295x.107.3.411
  48. J. A. Russell, “Core affect and the psychological construction of emotion.” Psychological review, vol. 110, no. 1, p. 145, 2003. http://dx.doi.org/10.1037/0033-295x.110.1.145
  49. E. Yaakobi, “Immediate moderating effects of attribution on ostracism distress,” Australian Psychologist, vol. 56, no. 3, pp. 193–203, 2021. http://dx.doi.org/10.1080/00050067.2020.1829452
  50. T. Y.-C. Yeung, “Did the covid-19 pandemic trigger nostalgia? evidence of music consumption on spotify,” Evidence of Music Consumption on Spotify (August 21, 2020), 2020.
  51. N. Henry, D. Kayser, and H. Egermann, “Music in mood regulation and coping orientations in response to covid-19 lockdown measures within the united kingdom,” Frontiers in psychology, vol. 12, p. 647879, 2021. http://dx.doi.org/10.3389/fpsyg.2021.647879
  52. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017. http://dx.doi.org/10.48550/arXiv.1706.03762
  53. M. O. Topal, A. Bas, and I. van Heerden, “Exploring transformers in natural language generation: Gpt, bert, and xlnet,” arXiv preprint https://arxiv.org/abs/2102.08036, 2021. http://dx.doi.org/10.48550/arXiv.2102.08036
  54. F. A. Acheampong, H. Nunoo-Mensah, and W. Chen, “Transformer models for text-based emotion detection: a review of bert-based approaches,” Artificial Intelligence Review, pp. 1–41, 2021. http://dx.doi.org/10.1007/s10462-021-09958-2
  55. J. Huang, J. Tao, B. Liu, Z. Lian, and M. Niu, “Multimodal transformer fusion for continuous emotion recognition,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/ICASSP40776.2020.9053762 pp. 3507–3511.
  56. D. Cortiz, “Exploring transformers in emotion recognition: a comparison of bert, distillbert, roberta, xlnet and electra,” arXiv preprint https://arxiv.org/abs/2104.02041, 2021. http://dx.doi.org/10.48550/arXiv.2104.02041
  57. J. S. Gómez-Cañón, E. Cano, T. Eerola, P. Herrera, X. Hu, Y.-H. Yang, and E. Gómez, “Music emotion recognition: Toward new, robust standards in personalized and context-sensitive applications,” IEEE Signal Processing Magazine, vol. 38, no. 6, pp. 106–114, 2021. http://dx.doi.org/10.1109/MSP.2021.3106232
  58. Y. Wang, J. Tian, D. S. Ones, R. N. Landers et al., “Using natural language processing and machine learning to replace human content coders.” Psychological Methods, 2022. http://dx.doi.org/10.1037/met0000518
  59. M. Alizadeh, M. Kubli, Z. Samei, S. Dehghani, J. D. Bermeo, M. Korobeynikova, and F. Gilardi, “Open-source large language models outperform crowd workers and approach chatgpt in text-annotation tasks,” arXiv preprint https://arxiv.org/abs/2307.02179, 2023. http://dx.doi.org/10.48550/arXiv.2307.02179
  60. M. Li, T. Shi, C. Ziems, M.-Y. Kan, N. F. Chen, Z. Liu, and D. Yang, “Coannotating: Uncertainty-guided work allocation between human and large language models for data annotation,” arXiv preprint https://arxiv.org/abs/2310.15638, 2023. http://dx.doi.org/10.18653/v1/2023.emnlpmain.92
  61. M. Hasanain, F. Ahmed, and F. Alam, “Large language models for propaganda span annotation,” arXiv preprint https://arxiv.org/abs/2311.09812, 2023. http://dx.doi.org/10.48550/arXiv.2311.09812
  62. P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pretrain, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys, vol. 55, no. 9, pp. 1–35, 2023. http://dx.doi.org/10.48550/arXiv.2107.13586
  63. S. Mohammadi, M. Mohammadi, S. Sinaei, A. Balador, E. Nowroozi, F. Flammini, and M. Conti, “Balancing privacy and accuracy in federated learning for speech emotion recognition,” in Proceedings of the 18th Conference on Computer Science and Intelligence Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, and D. Ślęzak, Eds., vol. 35. IEEE, 2023. http://dx.doi.org/http://dx.doi.org/10.15439/2023F444 p. 191–199.
  64. IBM, “Zero-shot learning,” 2024, accessed: 2024-07-20. [Online]. Available: https://www.ibm.com/topics/zero-shot-learning
  65. I. A. P. Santana, F. Pinhelli, J. Donini, L. Catharin, R. B. Mangolin, V. D. Feltrim, M. A. Domingues et al., “Music4all: A new music database and its applications,” in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2020. http://dx.doi.org/10.1109/IWSSIP48289.2020.9145170 pp. 399–404.
  66. K. D. Williams, C. K. Cheung, and W. Choi, “Cyberostracism: effects of being ignored over the internet.” Journal of personality and social psychology, vol. 79, no. 5, p. 748, 2000. http://dx.doi.org/https://psycnet.apa.org/doi/10.1037/0022-3514.79.5.748
  67. J. Borst, L. Wehrheim, A. Niekler, and M. Burghardt, “An evaluation of a zero-shot approach to aspect-based sentiment classification in historic german stock market reports,” in Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, and D. Ślęzak, Eds., vol. 37. PTI, 2023. http://dx.doi.org/http://dx.doi.org/10.15439/2023F3725 p. 51–60.
  68. D. Han, Y. Kong, J. Han, and G. Wang, “A survey of music emotion recognition,” Frontiers of Computer Science, vol. 16, no. 6, p. 166335, 2022. http://dx.doi.org/http://dx.doi.org/10.1007/s11704-021-0569-4
  69. M. de Witte, A. d. S. Pinho, G.-J. Stams, X. Moonen, A. E. Bos, and S. van Hooren, “Music therapy for stress reduction: a systematic review and meta-analysis,” Health Psychology Review, vol. 16, no. 1, pp. 134–159, 2022. http://dx.doi.org/https://doi.org/10.1080/17437199.2020.1846580