Affiliations: [a] Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran | [b] Department of Computer Engineering, Alzahra University, Vanak, Tehran, Iran
Correspondence:
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Corresponding author: Behrooz Masoumi, Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. E-mail: [email protected].
Abstract: Today, with the development of internet technology, a new kind of social relations and interactions have been formed in the newly emerged social networks. Through social networks, the users can share different types of content, including personal information, text, image, video, music, poem, and other related information, which express their mental states, emotions, feelings, and thoughts. Thus, a new and essential aspect of human life is being formed in a virtual space in social networks, which must be explored from several viewpoints, such as mental disorders. Analyzing mental disorders according to the social network data can guide us to gain new approaches to improve the public health of the whole society. To this aim, developing mental health feature extraction (MHFE) methods in a social network is essential and is now becoming an active research area. Therefore, in this paper, a review of existing techniques and methods in MHFE is presented, and a comprehensive framework is provided to classify these approaches. Furthermore, to analyze and evaluate each approach in extraction methods, an appropriate set of functional criteria is proposed, which leads to a more accurate understanding and correct use of them.
Keywords: Feature extraction, mental health, mental disorders, social network
