UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 11 | Issue 4 | April 2024

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 7 Issue 5
May-2020
eISSN: 2349-5162

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

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2005310


Registration ID:
231755

Page Number

48-52

Share This Article


Jetir RMS

Title

Psychological Illnesses Revealing Method Using Machine Learning

Abstract

A rising amount of Social Network Mental Disorders (SNMDs) are newly noted. One of the key solutions to this problem is detailed study of individual’s behavior attributes. These behavioral attributes can be extracted from various social networking sites such as Twitter, Facebook, etc. Social networking platform is best medium to know person’s behavior, thinking pattern, mood, opinions etc. The individuals on social media express their feelings, daily activities and opinions regarding various topics. So, social networking sites are used as screening tool to predict mental disorder stages. People opinions could be positive, negative or neutral. In order to determine mental disorder stages, person’s negative response is significant because it tells us about the negativism. Earlier method of diagnosis of patient through just psychological questionnaires is not so relevant but by using user generated content on social media definitely helps to predict stages of SNMD of particular individual. Our project aim is to extract information from social media posts and by having clear understanding of person’s behavioral attributes and attempted questionnaires, mental disorder stages of user can be detected. By using machine learning algorithm such as Naive Bayes, mental health status of a user is classified into three SNMD stages like stage 1, stage2, stage3. The results manifest that Social Network Mental Disorder Detection (SNMDD) is capable for identifying and distinguish online social network users with potential SNMDs from user not having mental disorder.

Key Words

Naïve Bayes Classifier, Social Network Activities, Text Mining, Anomalies, UGC (User generated Content)

Cite This Article

"Psychological Illnesses Revealing Method Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.48-52, May-2020, Available :http://www.jetir.org/papers/JETIR2005310.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

"Psychological Illnesses Revealing Method Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp48-52, May-2020, Available at : http://www.jetir.org/papers/JETIR2005310.pdf

Publication Details

Published Paper ID: JETIR2005310
Registration ID: 231755
Published In: Volume 7 | Issue 5 | Year May-2020
DOI (Digital Object Identifier):
Page No: 48-52
Country: pune, Maharastra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

0003028

Print This Page

Current Call For Paper

Jetir RMS