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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

Abdelwahed Motwakel1,*, Hala J. Alshahrani2, Jaber S. Alzahrani3, Ayman Yafoz4, Heba Mohsen5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Makkah, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
6 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Abdelwahed Motwakel. Email: email

Computer Systems Science and Engineering 2023, 47(3), 2741-2757. https://doi.org/10.32604/csse.2023.034721

Abstract

Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets. At the introductory level, the DHODBN-EC technique pre-processes the tweets at different levels. Besides, the word2vec feature extraction process is applied to generate the word embedding process. For emotion classification, the DHODBN-EC model utilizes the DBN model, which helps to determine distinct emotion class labels. Lastly, the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique. An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach. A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

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APA Style
Motwakel, A., Alshahrani, H.J., Alzahrani, J.S., Yafoz, A., Mohsen, H. et al. (2023). Deer hunting optimization with deep learning enabled emotion classification on english twitter data. Computer Systems Science and Engineering, 47(3), 2741-2757. https://doi.org/10.32604/csse.2023.034721
Vancouver Style
Motwakel A, Alshahrani HJ, Alzahrani JS, Yafoz A, Mohsen H, Yaseen I, et al. Deer hunting optimization with deep learning enabled emotion classification on english twitter data. Comput Syst Sci Eng. 2023;47(3):2741-2757 https://doi.org/10.32604/csse.2023.034721
IEEE Style
A. Motwakel et al., "Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 2741-2757. 2023. https://doi.org/10.32604/csse.2023.034721



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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