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

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.

@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},
  volume    = {11},
  number    = {12},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Roobaea Alroobaea and Sali Alafif and Shomookh Alhomidi and Ahad Aldahass and Reem Hamed and Rehab Mulla and Bedour Alotaibi},
  doi       = {10.14569/IJACSA.2020.0111245},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111245}
}