Advances in Sentiment and Emotion Analysis Techniques
DOI:
https://doi.org/10.56294/hl2024.399Keywords:
Emotion, Sentiment, Machine, Facial and speech dataAbstract
Introduction: Understanding and analyzing human emotions is a critical area of research, with applications spanning healthcare, education, entertainment, and human-computer interaction. Objective: Leveraging modalities such as facial expressions, speech patterns, physiological signals, and text data, this study examines the integration of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models, to capture intricate emotional cues effectively.
Method: This dataset offers a broad spectrum of emotional categories and sentiment classifications, serving as a robust resource for advancing innovative machine learning and deep learning models.
Result: The findings pave the way for developing intelligent systems capable of adapting to human emotions, fostering more natural and empathetic interactions between humans and machines.
Conclusion: Future directions include expanding datasets, addressing ethical considerations, and integrating these models into real-world applications.
References
1. Almrezeq N, Haque MA, Haque S, El-Aziz AAA. Device Access Control and Key Exchange (DACK) Protocol for Internet of Things. Int J Cloud Appl Comput [Internet]. 2022 Jan;12(1):1–14. Available from: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.297103
2. Zeba S, Haque MA, Alhazmi S, Haque S. Advanced Topics in Machine Learning. Mach Learn Methods Eng Appl Dev. 2022;197.
3. Whig V, Othman B, Gehlot A, Haque MA, Qamar S, Singh J. An Empirical Analysis of Artificial Intelligence (AI) as a Growth Engine for the Healthcare Sector. In: 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE; 2022. p. 2454–7.
4. Nandwani P, Verma R. A review on sentiment analysis and emotion detection from text. Soc Netw Anal Min. 2021;11(1):81.
5. Birjali M, Kasri M, Beni-Hssane A. A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Syst. 2021;226:107134.
6. Olisah CC, Smith L. Understanding unconventional preprocessors in deep convolutional neural networks for face identification. SN Appl Sci. 2019;1(11):1511.
7. Zhang T, Tan Z. Survey of deep emotion recognition in dynamic data using facial, speech and textual cues. Multimed Tools Appl. 2024;1–40.
8. Yadav A, Gupta A. An emotion-driven, transformer-based network for multimodal fake news detection. Int J Multimed Inf Retr. 2024;13(1):7.
9. Ahmed T, Qassem M, Kyriacou PA. Physiological monitoring of stress and major depression: A review of the current monitoring techniques and considerations for the future. Biomed Signal Process Control. 2022;75:103591.
10. Wang X, Chen X, Cao C. Human emotion recognition by optimally fusing facial expression and speech feature. Signal Process Image Commun. 2020;84:115831.
11. Zhao J, Mao X, Chen L. Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomed Signal Process Control. 2019;47:312–23.
12. Ghimire D, Jeong S, Lee J, Park SH. Facial expression recognition based on local region specific features and support vector machines. Multimed Tools Appl. 2017;76:7803–21.
13. Hamadani JD, Mehrin SF, Tofail F, Hasan MI, Huda SN, Baker-Henningham H, et al. Integrating an early childhood development programme into Bangladeshi primary health-care services: an open-label, cluster-randomised controlled trial. Lancet Glob Heal. 2019;7(3):e366–75.
14. Kalusivalingam AK, Sharma A, Patel N, Singh V. Enhancing Process Automation Using Reinforcement Learning and Deep Neural Networks. Int J AI ML. 2020;1(3).
15. Zhao Z, Li Y, Yang J, Ma Y. A lightweight facial expression recognition model for automated engagement detection. Signal, Image Video Process. 2024;18(4):3553–63.
16. Alam MS, Rashid MM, Faizabadi AR, Mohd Zaki HF, Alam TE, Ali MS, et al. Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI. Technologies. 2023;11(5):115.
17. sentiment-and-emotion-analysis-dataset [Internet]. Available from: https://www.kaggle.com/datasets/kushagra3204/sentiment-and-emotion-analysis-dataset
Published
Issue
Section
License
Copyright (c) 2024 Anamika Kumari, Smita Pallavi, Subhash Chandra Pandey (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
