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DOI: 10.14569/IJACSA.2020.0111245
PDF

A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments

Author 1: Roobaea Alroobaea
Author 2: Sali Alafif
Author 3: Shomookh Alhomidi
Author 4: Ahad Aldahass
Author 5: Reem Hamed
Author 6: Rehab Mulla
Author 7: Bedour Alotaibi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 12, 2020.

  • Abstract and Keywords
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Abstract: Author profiling aims to correlate writing style with author demographics. This paper presents an approach used to build a Decision Support System (DSS) for detecting age and gender from Twitter feeds. The system is implemented based on Deep Learning (DL) algorithms and Machine Learning (ML) algorithms to distinguish between classes of age and gender. The results show that every algorithm has different results of age and gender based on the model architecture and power points of each algorithm. Our decision support system is more accurate in predicting the age and the gender of author profiling from his\her written tweets. It adopts the deep learning model using CNN and LSTM methods. Our results outperform those obtained in the competitive conference s CLEF 2019.

Keywords: Decision support system; age detection; gender detection; author profiling; deep learning; machine learning

Roobaea Alroobaea, Sali Alafif, Shomookh Alhomidi, Ahad Aldahass, Reem Hamed, Rehab Mulla and Bedour Alotaibi, “A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111245

@article{Alroobaea2020,
title = {A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111245},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111245},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Roobaea Alroobaea and Sali Alafif and Shomookh Alhomidi and Ahad Aldahass and Reem Hamed and Rehab Mulla and Bedour Alotaibi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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