Identifying Users’ Emotional States through Keystroke Dynamics

Stefano Marrone, Carlo Sansone

2022

Abstract

Recognising users’ emotional states is among the most pursued tasks in the field of affective computing. Despite several works show promising results, they usually require expensive or intrusive hardware. Keystroke Dynamics (KD) is a behavioural biometric, whose typical aim is to identify or confirm the identity of an individual by analysing habitual rhythm patterns as they type on a keyboard. This work focuses on the use of KD as a way to continuously predict users’ emotional states during message writing sessions. In particular, we introduce a time-windowing approach that allows analysing users’ writing sessions in different batches, even when the considered writing window is relatively small. This is very relevant in the field of social media, where the exchanged messages are usually very small and the typing rhythm is very fast. The obtained results suggest that even very short writing windows (in the order of 30”) are sufficient to recognise the subject’s emotional state with the same level of accuracy of systems based on the analysis of larger writing sessions (i.e., up to a few minutes).

Paper Citation

in Harvard Style

Marrone S. and Sansone C. (2022). Identifying Users’ Emotional States through Keystroke Dynamics. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 207-214. DOI: 10.5220/0011367300003277

in Bibtex Style

@conference{delta22,
author={Stefano Marrone and Carlo Sansone},
title={Identifying Users’ Emotional States through Keystroke Dynamics},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011367300003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Identifying Users’ Emotional States through Keystroke Dynamics
SN - 978-989-758-584-5
AU - Marrone S.
AU - Sansone C.
PY - 2022
SP - 207
EP - 214
DO - 10.5220/0011367300003277