Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media

Hadj Ahmed Bouarara
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引用次数: 26

Abstract

A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behaviour in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia. The authors have adapted the recurrent neural network (RNN) in order to prevent the situations of threats, suicide, loneliness, or any other form of psychological problem through the analysis of tweets. The obtained results were validated by different experimental measures such as f-measure, recall, precision, entropy, accuracy. The RNN gives best results with 85% of accuracy compared to other techniques in literature such as social cockroaches, decision tree, and naïve Bayes.
递归神经网络(RNN)分析社交媒体中的心理行为
英国最近一项针对14至35岁人群的研究表明,社交媒体对心理健康有负面影响。这篇论文的目的是检测有精神障碍的人在社交媒体上的行为,以帮助Twitter用户克服他们的心理健康问题,如焦虑、恐惧症、抑郁、偏执。为了防止威胁、自杀、孤独或任何其他形式的心理问题,作者对循环神经网络(RNN)进行了改造。通过f-测度、召回率、精密度、熵、准确度等实验指标对所得结果进行了验证。与文献中的其他技术(如社会蟑螂、决策树和naïve贝叶斯)相比,RNN给出了最好的结果,准确率为85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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