Exploring Machine-learning Techniques for Early Detection of Depression from Social Media Posts

Nalini Singh, Rajnish Pandey, Praveen Mishra, Shashank S. Tiwari, Mariya Siddiqui

2021

Abstract

Different social media platforms are trendy among all age groups of people. They post their daily activities regarding the things which have happened to them. People also express their feelings which can be of any kind, such as depressive, sarcastic, irony, and many more. Identifying depression from those social media posts is very difficult work. This work has collected a dataset containing depressive and non-depressive tweets from Twitter and investigated different conventional machine-learning classifiers. Among all classifiers, the Support Vector Machine (SVM) performs better than the remaining and obtained an F1-score of 0.89

Paper Citation

in Harvard Style

Singh N., Pandey R., Mishra P., Tiwari S. and Siddiqui M. (2021). Exploring Machine-learning Techniques for Early Detection of Depression from Social Media Posts. In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE, ISBN 978-989-758-544-9, pages 22-27. DOI: 10.5220/0010561800003161

in Bibtex Style

@conference{icacse21,
author={Nalini Singh and Rajnish Pandey and Praveen Mishra and Shashank S. Tiwari and Mariya Siddiqui},
title={Exploring Machine-learning Techniques for Early Detection of Depression from Social Media Posts},
booktitle={Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,},
year={2021},
pages={22-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010561800003161},
isbn={978-989-758-544-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,
TI - Exploring Machine-learning Techniques for Early Detection of Depression from Social Media Posts
SN - 978-989-758-544-9
AU - Singh N.
AU - Pandey R.
AU - Mishra P.
AU - Tiwari S.
AU - Siddiqui M.
PY - 2021
SP - 22
EP - 27
DO - 10.5220/0010561800003161