Abstract
For humans, meals are significant, not only to intake nutrients or feel satisfaction but also to feel connected with family and society through interpersonal communication. This study aimed to estimate and examine the psychological states and traits of texts describing eating experiences using BERT. Texts about positive, negative, and neutral eating experiences were collected from 877 crowd workers along with their psychological traits (loneliness and depression). The accuracy of the 6-label classification of the three psychological states × two eating situations (co-eating or eating alone) was 72%. Although the accuracies of the binary classification of loneliness and depression are approximately 55%, they are comparable with those of crowd workers. These results suggest that estimating psychological traits is more difficult than estimating psychological states from a single text per crowd worker. Further analyses revealed that the fine-tuned BERT classifiers of psychological traits use language features different from those of human raters.
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References
Higgs, S., Thomas, J.: Social influences on eating. Curr. Opin. Behav. Sci. 9, 1–6 (2016)
Boothby, E.J., Clark, M.S., Bargh, J.A.: Shared experiences are amplified. Psychol. Sci. 25(12), 2209–2216 (2014)
Yukako, T., et al.: Combined effect of eating alone and living alone on unhealthy dietary behaviors, obesity and underweight in older Japanese adults: results of the JAGES. Appetite 95, 1–8 (2015)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics (2019)
Russell, D.W.: The UCLA loneliness scale (version3): reliability, validity, and factor structure. J. Pers. Assess. 66(1), 20–40 (1996)
Satoru, S., Tatsuo, S., Toshinori, K., Masahiro, A.: New self-rating scale for depression. Clin. Psychiatry 27, 717–723 (1985)
Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, pp. 1631–1642. Association for Computational Linguistics (2013)
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, pp. 1556–1566. Association for Computational Linguistics (2015)
Munikar, M., Shakya, S., Shrestha, A.: Fine-grained sentiment classification using BERT. In: 2019 Artificial Intelligence for Transforming Business and Society (AITB), pp. 1–5 (2019)
https://nlp.ist.i.kyoto-u.ac.jp/?JUMAN%2B%2B. Accessed 22 Oct 2022
https://nlp.ist.i.kyoto-u.ac.jp/?KNP. Accessed 22 Oct 2022
https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking. Accessed 22 Oct 2022
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Nakai, K., Iwai, R., Kumada, T. (2023). An Examination of Eating Experiences in Relation to Psychological States, Loneliness, and Depression Using BERT. In: Takama, Y., Yada, K., Satoh, K., Arai, S. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2022. Lecture Notes in Computer Science(), vol 13859. Springer, Cham. https://doi.org/10.1007/978-3-031-29168-5_14
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DOI: https://doi.org/10.1007/978-3-031-29168-5_14
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