Psychological Stress Detection from Social Media Data using a Novel Hybrid Model
DOI:
https://doi.org/10.18201/ijisae.2018448457Keywords:
Psychological Stress Detection, Social Media interaction, Health agencies, Physiological SignalsAbstract
Psychological stress is considered as the biggest threat to individual’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming, complex and expensive. With the rapid growth of social networks, people are willing to share moods via social media platforms making it practicable to leverage online social interaction data for stress detection. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the individual’s psychological stress from social media. It comprises of three modules Probabilistic Naïve Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information we have defined the set of stress-related textual ‘F = {f1, f2, f3, f4}’, visual ‘vF = {vf1, vf2}’, social ‘sf’ to detect and predict stress from social media content. Experimental results show that the proposed PSD model improves the detection performance when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals.Downloads
References
Prof. Roohie Naaz Mir
dept of cse
Dept. Phone Number: Phone: 0194 - 2424792
email:naaz310@nitsri.net (Srinagar)
http://www.nitsri.net/cse/faculty_cse.pdf
Dr. Bilal Maqbool
Dept of IT Central University of Kashmir, Sonwar
(Central University of Kashmir)
Phone No: 9018277711
Email:bilal.beigh@gmail.com
https://www.cukashmir.ac.in/faculty_profile.aspx?sid=29&did=15&pag=112&id=203
Dr. MUQSIT KHAN
dept of cse,
manu,
POLYTECHNIC
PRINCIPAL
DHARBANGA
BIHAR
mobile: 09430013617
abdul_muqsit_khan@yahoo.co.in
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.