Haiyan WU , Cuilin HE , Youzhi QU , Quanying LIU . Emotional intelligence of large language models and its psychological applications[J]. Science & Technology Review, 2025 , 43(3) : 47 -58 . DOI: 10.3981/j.issn.1000-7857.2023.09.01352

1	Mayer J D , Roberts R D , Barsade S G . Human abilities: Emotional intelligence[J]. Annual Review of Psychology, 2008, 59: 507- 536. DOI

2	Salovey P , Mayer J D . Emotional intelligence[J]. Imagination, Cognition and Personality, 1990, 9 (3): 185- 211. DOI

3	Carmeli A , Yitzhak-Halevy M , Weisberg J . The relationship between emotional intelligence and psychological wellbeing[J]. Journal of Managerial Psychology, 2009, 24 (1): 66- 78. DOI

4	Lopes P N , Salovey P , Straus R . Emotional intelligence, personality, and the perceived quality of social relationships[J]. Personality and Individual Differences, 2003, 35 (3): 641- 658. DOI

5	Petrides K V , Frederickson N , Furnham A . The role of trait emotional intelligence in academic performance and deviant behavior at school[J]. Personality and Individual Differences, 2004, 36 (2): 277- 293. DOI

6	Urquijo I , Extremera N , Azanza G . The contribution of emotional intelligence to career success: Beyond personality traits[J]. International Journal of Environmental Research and Public Health, 2019, 16 (23): 4809. DOI

7	Bourne P , Hanna I , Solomon T , et al. Primal leadership: Realizing the power of emotional intelligence: A book review[J]. COJ Nursing & Healthcar, 2017, 1 (2): 46- 50.

8	Schutte N S, Malouff J M, Bhullar N. The assessing emotions scale[M]//Assessing Emotional Intelligence. Boston, MA: Springer, 2009: 119-134.

9	Mayer J D , Salovey P , Caruso D R , et al. Measuring emotional intelligence with the MSCEIT V2.0[J]. Emotion, 2003, 3 (1): 97- 105. DOI

10	Keightley M L , Winocur G , Graham S J , et al. An fMRI study investigating cognitive modulation of brain regions associated with emotional processing of visual stimuli[J]. Neuropsychologia, 2003, 41 (5): 585- 596. DOI

11	Koster E H W , Crombez G , Verschuere B , et al. Selective attention to threat in the dot probe paradigm: Differentiating vigilance and difficulty to disengage[J]. Behaviour Research and Therapy, 2004, 42 (10): 1183- 1192. DOI

12	Williams J M , Mathews A , MacLeod C . The emotional Stroop task and psychopathology[J]. Psychological Bulletin, 1996, 120 (1): 3- 24. DOI

13	Agarwal A, Xie B, Vovsha I, et al. Sentiment analysis of twitter data[C]//Proceedings of the Workshop on Language in Social Media. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011: 30-38.

14	Feldman R . Techniques and applications for sentiment analysis[J]. Communications of the ACM, 2013, 56 (4): 82- 89. DOI

15	Medhat W , Hassan A , Korashy H . Sentiment analysis algorithms and applications: A survey[J]. Ain Shams Engineering Journal, 2014, 5 (4): 1093- 1113. DOI

16	Minaee S , Kalchbrenner N , Cambria E , et al. Deep learning: Based text classification[J]. ACM Computing Surveys, 2022, 54 (3): 1- 40.

17	Zhang L , Wang S , Liu B . Deep learning for sentiment analysis: A survey[J]. WIREs Data Mining and Knowledge Discovery, 2018, 8 (4): e1253. DOI

18	Cahyawijaya S, Chen D L, Bang Y J, et al. High-dimension human value representation in large language models[EB/OL]. [2024-03-05]. https://arxiv.org/abs/2404.07900v3.

19	Serapio-García G, Safdari M, Crepy C, et al. Personality traits in large language models[EB/OL]. [2024-03-05]. https://arxiv.org/abs/2307.00184v3.

20	Xie Q Z, Dai Z H, Hovy E, et al. Unsupervised data augmentation for consistency training[C]//Proceedings of Advances in Neural Information Processing Systems. New York: Curran Associates, 2020: 6256-6268.

21	Zhang X, Zhao J B, LeCun Y. Character-level convolutional networks for text classification[C]//Proceedings of Advances in Neural Information Processing Systems. New York: Curran Associates, 2015: 649-657.

22	Shahraki A G, Zaiane O R. Lexical and learning-based emotion mining from text[C]//Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing. Switzerland: Springer, 2017: 24-55.

23	Scherer K R , Wallbott H G . Evidence for universality and cultural variation of differential emotion response patterning[J]. Journal of Personality and Social Psychology, 1994, 66 (2): 310- 328. DOI

24	Socher R, Perelygin A, Wu J Y, et al. Recursive deep models for semantic compositionality over a sentiment treebank [C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2013: 1631-1642.

25	Li M, Long Y, Qin L, et al. Emotion corpus construction based on selection from hashtags[C]//Proceedings of the Tenth International Conference on Language Resources and Evaluation. Portorož, Slovenia: European Language Resources Association, 2016: 1845-1849.

26	姚源林, 王树伟, 徐睿峰, 等. 面向微博文本的情绪标注语料库构建[J]. 中文信息学报, 2014, 28 (5): 83- 91. DOI

27	Huang Y X, Jusoh S. Sentiment detection through emotion classification using deep learning approach for Chinese text [C]//Proceedings of 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). Piscataway, NJ: IEEE, 2023: 1-6.

28	Xing F. Designing heterogeneous LLM agents for financial sentiment analysis[EB/OL]. [2024-03-05]. https://arxiv.org/abs/2401.05799v1.

29	Sun X F, Li X Y, Zhang S Y, et al. Sentiment analysis through LLM negotiations[EB/OL]. [2024-04-06]. https://arxiv.org/abs/2311.01876v1.

30	Kang I, Ruan S K, Ho T, et al. LLM-augmented preference learning from natural language[EB/OL]. [2024-05-05]. https://arxiv.org/abs/2310.08523v1.

31	Cao S Q , Fu D , Yang X , et al. Pain recognition and pain empathy from a human-centered AI perspective[J]. iScience, 2024, 27 (8): 110570. DOI

32	Qu Y Z , Du P H , Che W X , et al. Promoting interactions between cognitive science and large language models[J]. The Innovation, 2024, 5 (2): 100579. DOI

33	Liu Z W, Yang K L, Xie Q Q, et al. EmoLLMs: A series of emotional large language models and annotation tools for comprehensive affective analysis[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2024: 5487-5496.

34	Ouyang L, Wu J, Xu J, et al. Training language models to follow instructions with human feedback[C]//Proceedings of Advances in Neural Information Processing Systems. New York: Curran Associates, 2022: 27730-27744.

35	Albert S . Cognition and emotion[M]. Oxford: Oxford University Press, 2000.

36	Wilson M . Six views of embodied cognition[J]. Psychonomic Bulletin & Review, 2002, 9 (4): 625- 636.

37	Niedenthal P M , Barsalou L W , Winkielman P , et al. Embodiment in attitudes, social perception, and emotion[J]. Personality and Social Psychology Review, 2005, 9 (3): 184- 211. DOI

38	Romera-Paredes B , Barekatain M , Novikov A , et al. Mathematical discoveries from program search with large language models[J]. Nature, 2024, 625 (7995): 468- 475. DOI

39	Thirunavukarasu A J , Ting D S J , Elangovan K , et al. Large language models in medicine[J]. Nature Medicine, 2023, 29 (8): 1930- 1940. DOI

40	Demszky D , Yang D Y , Yeager D S , et al. Using large language models in psychology[J]. Nature Reviews Psychology, 2023, 2: 688- 701.

41	Wang X N , Li X T , Yin Z , et al. Emotional intelligence of large language models[J]. Journal of Pacific Rim Psychology, 2023, 17: 1- 12.

42	Xi Z H, Chen W X, Guo X, et al. The rise and potential of large language model based agents: A survey[EB/OL]. [2024-05-05]. https://arxiv.org/abs/2309.07864v3.

43	Amin M M , Cambria E , Schuller B W . Will affective computing emerge from foundation models and general artificial intelligence? A first evaluation of ChatGPT[J]. IEEE Intelligent Systems, 2023, 38 (2): 15- 23. DOI

44	Zhang Z , Peng L , Pang T , et al. Refashioning emotion recognition modelling: The advent of generalised large models[J]. IEEE Transactions on Computational Social Systems, 2024, 11 (5): 6690- 6704. DOI

45	Yang H , Zi Y , Qin H , et al. Advancing emotional analysis with large language models[J]. Journal of Computer Science and Software Applications, 2024, 4 (3): 8- 15.

46	Xu Y, Chen H, Yu J, et al. Secap: Speech emotion captioning with large language model[C]//Proceedings of the AAAI Conference on Artificial Intelligence. California, USA: Association for the Advancement of Artificial Intelligence, 2024: 19323-19331.

47	Li M, Su Y S, Huang H Y, et al. Language-specific representation of emotion-concept knowledge causally supports emotion inference[EB/OL]. [2024-05-05]. https://arxiv.org/abs/2302.09582v5.

48	Huang J T, Lam M H, Li E J, et al. Emotionally numb or empathetic? evaluating how LLMs feel using EmotionBench [EB/OL]. [2024-06-10]. https://arxiv.org/abs/2308.03656v6.

49	Liu J L, Liu C, Zhou P L, et al. Is ChatGPT a good recommender? A preliminary study[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2304.10149v3.

50	Lian Z, Sun H Y, Sun L C, et al. Explainable multimodal emotion recognition[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2306.15401v6.

51	Li C, Wang J, Zhu K J, et al. Large language models understand and can be enhanced by emotional stimuli[EB/OL]. [2023-08-01]. https://arxiv.org/abs/2307.11760v7

52	Schramowski P , Turan C , Andersen N , et al. Large pretrained language models contain human-like biases of what is right and wrong to do[J]. Nature Machine Intelligence, 2022, 4 (3): 258- 268. DOI

53	Motoki F , Pinho Neto V , Rodrigues V . More human than human: Measuring ChatGPT political bias[J]. Public Choice, 2024, 198 (1): 3- 23.

54	Qiu H C, He H L, Zhang S, et al. SMILE: Single-turn to multi-turn inclusive language expansion via ChatGPT for mental health support[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2305.00450v3.

55	Krügel S, Ostermaier A, Uhl M. The moral authority of ChatGPT[EB/OL]. [2024-09-10]. https://arxiv.org/abs/2301.07098v1.

56	Kotek H, Dockum R, Sun D. Gender bias and stereotypes in large language models[C]//Proceedings of The ACM Collective Intelligence Conference. New York: ACM, 2023: 12-24.

57	Coda-Forno J, Witte K, Jagadish A K, et al. Inducing anxiety in large language models can induce bias[EB/OL]. [2024-09-11]. https://arxiv.org/abs/2304.11111v2.

58	矣晓沅, 谢幸. 大模型道德价值观对齐问题剖析[J]. 计算机研究与发展, 2023, 60 (9): 1926- 1945.

59	Takemoto K . The moral machine experiment on large language models[J]. Royal Society Open Science, 2024, 11 (2): 231393. DOI

60	Zhou J Y, Hu M D, Li J N, et al. Rethinking machine ethics: Can LLMs perform moral reasoning through the lens of moral theories? [EB/OL]. [2024-09-11]. https://arxiv.org/abs/2308.15399v2.

61	Alhassan A, Zhang J, Schlegel V. "Am I the Bad One"? Predicting the Moral Judgement of the Crowd Using Pretrained Language Models[C]//Proceedings of the Thirteenth Language Resources and Evaluation Conference. Marseille, France: European Language Resources Association, 2022: 267-276.

62	Scherrer N, Shi C, Feder A, et al. Evaluating the moral beliefs encoded in llms[C]//Proceedings of Advances in Neural Information Processing Systems. New York: Curran Associates, 2023: 51778-51809.

63	Yang K L, Ji S X, Zhang T L, et al. Towards interpretable mental health analysis with large language models[C]//Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023: 6056-6077.

64	Van Heerden A C , Pozuelo J R , Kohrt B A . Global mental health services and the impact of artificial intelligencepowered large language models[J]. JAMA Psychiatry, 2023, 80 (7): 662- 664. DOI

65	Bender E M, Gebru T, McMillan-Major A, et al. On the dangers of stochastic parrots: Can language models be too big?[C]//Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York: ACM, 2021: 610-623.

66	Liu Y, Yao Y S, Ton J F, et al. Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment[EB/OL]. [2024-09-11]. https://arxiv.org/abs/230-8.05374v2.

67	Li C, Fung M, Wang Q, et al. MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders[EB/OL]. [2024-09-11]. https://arxiv.org/abs/2410.06845v1.

68	Kjell O N E , Kjell K , Schwartz H A . Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment[J]. Psychiatry Research, 2024, 333: 115667. DOI

69	Götz F M , Maertens R , Loomba S , et al. Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development[J]. Psychological Methods, 2024, 29 (3): 494- 518. DOI

70	Huang S H, Dong L, Wang W H, et al. Language is not all you need: Aligning perception with language models[C]//Proceedings of Advances in Neural Information Processing Systems. New York: Curran Associates, 2023: 72096-72109.

71	Linear-probe A. Learning transferable visual models from natural language supervision[C]//Proceedings of International Conference on Machine Learning. New York: ACM, 2021: 8748-8763.

72	Zhu X X , Guo C P , Feng H Y , et al. A review of key technologies for emotion analysis Using Multimodal information[J]. Cognitive Computation, 2024, 16 (4): 1504- 1530.

73	Vaiani L, Quatra M L, Cagliero L, et al. Viper: Video-based perceiver for emotion recognition[C]//Proceedings of the 3rd International on Multimodal Sentiment Analysis Workshop and Challenge. New York: ACM, 2022: 67-73.

74	Goh G , Cammarata N , Voss C , et al. Multimodal neurons in artificial neural networks[J]. Distill, 2021, 6 (3): e30.

75	Bondielli A, Passaro L C. Leveraging CLIP for image emotion recognition[C]//Proceedings of the 5th Workshop on Natural Language for Artificial Intelligence. Aachen, Germany: CEUR-WS, 2021: 172-185.

76	Zhang S T, Pan Y M, Wang J Z. Learning emotion representations from verbal and nonverbal communication[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, 2023: 18993-19004.