文章基本信息

标题：A Decision Support System for Detecting Age and Gender from Twitter Feeds based on a Comparative Experiments
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作者：Roobaea Alroobaea ; Sali Alafif ; Shomookh Alhomidi 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：12
页码：370-376
DOI：10.14569/IJACSA.2020.0111245
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Decision support system; age detection; gender detection; author profiling; deep learning; machine learning