Data mining and related topics
Ramin Safa; Peyman Bayat; Leila Moghtader
Abstract
Purpose: While diagnosing mental disorders in traditional approaches relies on questionnaires, interviews, and clinical trials, automated screening tools can take a shorter path. These tools can be developed as innovative evaluation techniques, decision support systems, and prevention strategies to help ...
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Purpose: While diagnosing mental disorders in traditional approaches relies on questionnaires, interviews, and clinical trials, automated screening tools can take a shorter path. These tools can be developed as innovative evaluation techniques, decision support systems, and prevention strategies to help susceptible individuals. Due to the tendency of people to share thoughts and feelings on social platforms, microblogging data contains valuable information that can be analyzed to identify users’ mental states. This study describes a roadmap for data analysis in the field in question.Methodology: In the first part of this paper, concepts such as electronic mental health and microblogging platforms are introduced. And their conceptual relationship is discussed by providing explanations about data science and social data analysis. Next, the prediction of disorder in social platforms is described separately. Finally, by reviewing related works and open issues, we explain how data collection, pre-processing, and analysis are done using different features of real-world data.Findings: By experimental analysis, this study shows that the extracted features from the users’ profiles have a significant effect on predicting mental health symptoms, and even with the information extracted from the users’ public profiles, the mental state can be predicted with reasonable accuracy.Originality/Value: In this study, automatic analysis of social data to investigate the psychological signals is described, and in the implementation section, it is founded that the symptoms can be followed in almost all the studied features.