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Identifying Users' Emotional States through Keystroke Dynamics

: 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).