Crowd-Based Profiling : A Framework to Detect Psychological Disorders in Social Media Users
A.Sharmila Agnal1, Akshay Kannan V2, Durga S3, Nishanth Kumar. S4, Dheeraj. R5
1A Sharmila Agnal, Department of CSE, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
2Akshay Kannan V, Department of CSE, SRM Institute of Science and Technology, Chennai, (Tamil Nadu), India.
3Durga S, Department of CSE, SRM Institute of Science and Technology, (Tamil Nadu), India.
4Nishanth Kumar S, Department of CSE, SRM Institute of Science and Technology, Vellore, (Tamil Nadu), India.
5Dheeraj R, Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1137-1140 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2539037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Psychological disorders are presently striking a large number of population from various civilization, society, occupation and different locations around the world. The main obstacle of psychological disorders is the difficulty to detect on people suffering from these disorders, hence resulting in introducing a worrying amount of undetectable cases and false detection issue. Our methodology aims at constructing detective models to identify psychological disorders among online social media users. These detective models are attainable by engaging a basic data collection process formulated as crowd based profiling, which assists us to collect accurate and more efficient data set of people from various categories. Our experiment proposes that obtaining specific English language patterns and socializing attributes from data sets paves the way to deal with advanced experiments on psychological disorders.
Keywords: Crowd based profiling, Data sets, Online social media, Psychological disorders detection, Sentiment analysis.
Scope of the Article: Social Networks