UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 4
April-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2004352


Registration ID:
230957

Page Number

1142-1145

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Title

Social Sites Mental Disorders Detection via Online Social Media Mining using Machine Learning Framework

Abstract

Development in social network communication encourages dangerous use. An increasing number of mental disorders in social networks, such as cybernetic dependency, information overload and network constraint, have recently been observed. Currently, the symptoms of these mental illness are passively observed, which causes late clinical intervention. In this paper, we argue that online social behaviour mining offers the chance to effectively recognize mental disorder at a beginning period. It is difficult to detect disorder because the mental state cannot be observed directly from the registers of online social activities. Our new and innovative approach to the act of disorder detection is not based on self-disclosure of these mental factors through psychology questionnaires. Instead, we propose a framework of machine learning or the detection of mental disorders in social networks, which exploits the features extracted from social network data to accurately identify possible disorder cases. We also use multiple learning sources in social network mental disorder and propose a new mental disorder based model to improve accuracy. Our framework is evaluated through a user study with none of the users on the network. We performed an analysis of the characteristics and we also applied the new proposed approach in large-scale data series and analysed the characteristics of the three types of mental disorder.

Key Words

Online social networking sites (OSN), Psychological mental disorder detection, feature extraction, SNMD Classifier.

Cite This Article

"Social Sites Mental Disorders Detection via Online Social Media Mining using Machine Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1142-1145, April-2020, Available :http://www.jetir.org/papers/JETIR2004352.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Social Sites Mental Disorders Detection via Online Social Media Mining using Machine Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1142-1145, April-2020, Available at : http://www.jetir.org/papers/JETIR2004352.pdf

Publication Details

Published Paper ID: JETIR2004352
Registration ID: 230957
Published In: Volume 7 | Issue 4 | Year April-2020
DOI (Digital Object Identifier):
Page No: 1142-1145
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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