Artificial Intelligence based Emotional Intelligence for data Analytics

Authors

  • Indra Kesuma, Zainuddin, Sofiyan, Salman Faris

Keywords:

Artificial Intelligence (AI), Emotional Intelligence (EI), Data Analytics; Decision-Making Processes,

Abstract

In recent years, the integration of Artificial Intelligence (AI) and Emotional Intelligence (EI) has emerged as a promising avenue for enhancing data analytics. Emotional Intelligence, a vital human trait involving the recognition, understanding, and regulation of emotions, offers a unique dimension to AI-driven analytics by infusing machines with empathetic capabilities. This paper explores the convergence of AI and EI in the context of data analytics, elucidating how incorporating emotional understanding into AI systems can revolutionize data interpretation and decision-making processes. The primary focus of this paper is to delineate the potential benefits and challenges associated with leveraging emotional intelligence in data analytics through AI algorithms. By harnessing EI, AI systems can better comprehend human emotions expressed in textual data, social media interactions, and other unstructured sources, thereby providing deeper insights into consumer sentiment, market trends, and user behavior. Furthermore, AI-driven emotional intelligence can enhance personalized recommendations, improve customer service interactions, and facilitate more empathetic human-machine interactions.

Downloads

Download data is not yet available.

References

Goleman, D. (1995). Emotional Intelligence: Why It Can Matter More Than IQ. Bantam Books.

Picard, R. W. (1997). Affective Computing. MIT Press.

Sharma, R., & Yadav, N. (2019). A Review on the Role of Emotional Intelligence in Artificial Intelligence. International Journal of Scientific & Engineering Research, 10(10), 242-246.

Ma, Y., Chen, Y., & Huang, M. (2020). Emotional Intelligence Empowered Textual Emotion Analysis: A Review. ACM Computing Surveys, 53(2), 1-38.

Kim, J. W., Zhang, Y., & Oh, A. H. (2021). Emotion Recognition and Its Applications in Affective Computing: A Systematic Review. Journal of Imaging Science and Technology, 65(4), 40301.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.

Cacioppo, J. T., & Patrick, W. (2008). Loneliness: Human Nature and the Need for Social Connection. W.W. Norton & Company.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019). Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220-229).

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689-707.

European Commission. (2018). Ethics Guidelines for Trustworthy AI. European Commission.

Zeng, H., Wen, J., & Zhai, K. (2021). Emotional Intelligence in Artificial Intelligence Systems: A Survey. Information Fusion, 73, 133-149.

Goldberg, Y. (2016). A Primer on Neural Network Models for Natural Language Processing. Journal of Artificial Intelligence Research, 57, 345-420.

Tadelis, S. (2016). Reputation and Feedback Systems in Online Platform Markets. Annual Review of Economics, 8, 321-340.

Martin, J. W., & White, A. P. (2005). The Edge: Is the Concept of Emotional Intelligence a Useful Tool for Managerial Succession Planning within Organisations? Journal of Management Development, 24(10), 929-941.

McEwen, B. S. (2015). Stress: The Role of Homeostasis, Allostasis, and Allostatic Load. Annals of the New York Academy of Sciences, 1312(1), 28-40.

Downloads

Published

26.03.2024

How to Cite

Zainuddin, Sofiyan, Salman Faris, I. K. . (2024). Artificial Intelligence based Emotional Intelligence for data Analytics. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1570–1574. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/5629

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.