Identifying Depression Clues using Emotions and AI

Ricardo Martins, José Almeida, Pedro Henriques, Paulo Novais

Abstract

According to the World Health Organization (WHO), close to 300 million people of all ages suffer from depression. Also, for WHO, depression is the leading reason for disability worldwide and is a major contributor to the global burden of disease. Different than the mood fluctuation raised by the common life’s activities, depression can be a serious health problem, particularly when it is a long-term and mid/high intensity. Luckily, despite depression is a silent disease, people when suffering leaves some clues. Due to the massive use of social media, these clues can be collected through the texts posted on social media, such as Twitter, Facebook, Instagram, and later, analysed to identify if the writing style matches with a depressive pattern. This paper presents an approach that can be applied by Machine Learning models to help psychologists to identify depressive clues in texts. The model examines profiles on Twitter based on clues provided by users in their posts. Combining Sentiment Analysis, Machine Learning and Natural Language Processing techniques, we achieved a precision of 98% by Machine Learning models when identifying Twitter profiles that post potential depressive texts.

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Paper Citation


in Harvard Style

Martins R., Almeida J., Henriques P. and Novais P. (2021). Identifying Depression Clues using Emotions and AI.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1137-1143. DOI: 10.5220/0010332811371143


in Bibtex Style

@conference{icaart21,
author={Ricardo Martins and José Almeida and Pedro Henriques and Paulo Novais},
title={Identifying Depression Clues using Emotions and AI},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1137-1143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010332811371143},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Identifying Depression Clues using Emotions and AI
SN - 978-989-758-484-8
AU - Martins R.
AU - Almeida J.
AU - Henriques P.
AU - Novais P.
PY - 2021
SP - 1137
EP - 1143
DO - 10.5220/0010332811371143