Published October 4, 2023
| Version v1
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DEFINITION OF TECHNIQUES FOR EMOTIONAL STATE ASSESSMENT
Creators
- 1. Doctoral student of the Department of Computer Science and Programming of the Jizzakh branch of the National University of Uzbekistan named after Mirzo Ulugbek
Description
This article aims to provide algorithmic insights into the evaluation of human emotions, highlighting the progress that has been made and the challenges that still exist. By utilizing machine learning algorithms and sentiment analysis, researchers have been able to uncover valuable information about the emotions that robots can express and how they impact consumers. This cross-disciplinary study paves the way for next-level social, design, and creative experiences in artificial intelligence research, particularly in the realms of consumer service and experience contexts.
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Additional details
References
- 1. KURBANOV A.A. Multimodal emotion recognition: a comprehensive survey with deep learning. Journal of Research and Innovation, pp. 43-47. 2023 2. Kurbanov Abdurahmon Alishboyevich. A Methodological Approach to Understanding Emotional States Using Textual Data. Journal of Universal Science Research. 2023 3. Kurbanov Abdurahmon. AI MODELS OF AFFECTIVE COMPUTING. International Conference of Contemporary Scientific and Technical Research. 2023 4. Kurbanov Abdurahmon Alishboyevich. USING AFFECTIVE COMPUTING SYSTEMS IN MODERN EDUCATION. Journal Science and innovation. 2023 5. Atzeni, Recupero, 2020 M. Atzeni, D.R. Recupero Multi-domain sentiment analysis with mimicked and polarized word embeddings for human–robot interaction. Future Generat. Comput. Syst., 110 (2020), pp. 984-999 6. Kamolov, Dostonbek Rustam O'G'Li. "O'ZBEKISTONDA DEMOKRATIYA VA AXLOQNING ZAMONAVIY MUAMMOLARI VA YECHIMLARI." Academic research in educational sciences 3.NUU Conference 2 (2022): 348-352. 7. Chatterjee et al., 2019 A. Chatterjee, G. Umang, K.C. Manoj, S. Radhakrishnan, G. Michel, A. Puneet Understanding emotions in text using deep learning and big data Comput. Hum. Behav., 93 (2019), pp. 309-317 8. Faraj et al., 2020 Z. Faraj, M. Selamet, C. Morales, P. Torres, M. Hossain, H. Lipson Facially Expressive Humanoid Robotic Face HardwareX (2020), Article e00117 9. Prottasha NJ, Sami AA, Kowsher M, Murad SA, Bairagi AK, Masud M, et al. Transfer learning for sentiment analysis using BERT based supervised fine-tuning. Sensors. 2022;22(11):4157 10. Tan KL, Lee CP, Lim KM, Anbananthen KSM. Sentiment analysis with ensemble hybrid deep. IEEE Access. 2022;10:103694-103704. Available from: https://doaj.org/article/948b7ca90291416fb31bda6b789b8920