Open Access iconOpen Access

ARTICLE

crossmark

Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

Badriyya B. Al-onazi1, Mohamed K. Nour2, Hassan Alshamrani3, Mesfer Al Duhayyim4,*, Heba Mohsen5, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6, Abu Sarwar Zamani6

1 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Makkah, 24211, Saudi Arabia
3 Department of Teachers Training, Arabic Linguistics Institute, King Saud University, P.O. BOX 145111, Riyadh, ZIP 4545, Saudi Arabia
4 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Al-Aflaj, 16733, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Mesfer Al Duhayyim. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 2529-2544. https://doi.org/10.32604/iasc.2023.034623

Abstract

Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter (OMSDAE-GIAT) model. The presented OMSDAE-GIAR technique mainly concentrates on the identification and classification of gender exist in the Twitter data. To attain this, the OMSDAE-GIAT model derives initial stages of data pre-processing and word embedding. Next, the MSDAE model is exploited for the identification of gender into two classes namely male and female. In the final stage, the OMSDAE-GIAT technique uses enhanced bat optimization algorithm (EBOA) for parameter tuning process, showing the novelty of our work. The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics. The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

Keywords


Cite This Article

APA Style
Al-onazi, B.B., Nour, M.K., Alshamrani, H., Duhayyim, M.A., Mohsen, H. et al. (2023). Gender identification using marginalised stacked denoising autoencoders on twitter data. Intelligent Automation & Soft Computing, 36(3), 2529-2544. https://doi.org/10.32604/iasc.2023.034623
Vancouver Style
Al-onazi BB, Nour MK, Alshamrani H, Duhayyim MA, Mohsen H, Abdelmageed AA, et al. Gender identification using marginalised stacked denoising autoencoders on twitter data. Intell Automat Soft Comput . 2023;36(3):2529-2544 https://doi.org/10.32604/iasc.2023.034623
IEEE Style
B.B. Al-onazi et al., "Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data," Intell. Automat. Soft Comput. , vol. 36, no. 3, pp. 2529-2544. 2023. https://doi.org/10.32604/iasc.2023.034623



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.
  • 967

    View

  • 522

    Download

  • 0

    Like

Share Link