Keystroke Dynamics Patterns While Writing Positive and Negative Opinions.
affect analysis
behavioural patterns
emotion recognition
keystroke dynamics
opinion mining
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
06 Sep 2021
06 Sep 2021
Historique:
received:
28
07
2021
revised:
20
08
2021
accepted:
30
08
2021
entrez:
10
9
2021
pubmed:
11
9
2021
medline:
14
9
2021
Statut:
epublish
Résumé
This paper deals with analysis of behavioural patterns in human-computer interaction. In the study, keystroke dynamics were analysed while participants were writing positive and negative opinions. A semi-experiment with 50 participants was performed. The participants were asked to recall the most negative and positive learning experiences (subject and teacher) and write an opinion about it. Keystroke dynamics were captured and over 50 diverse features were calculated and checked against the ability to differentiate positive and negative opinions. Moreover, classification of opinions was performed providing accuracy slightly above the random guess level. The second classification approach used self-report labels of pleasure and arousal and showed more accurate results. The study confirmed that it was possible to recognize positive and negative opinions from the keystroke patterns with accuracy above the random guess; however, combination with other modalities might produce more accurate results.
Identifiants
pubmed: 34502854
pii: s21175963
doi: 10.3390/s21175963
pmc: PMC8434638
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : EEA Grants/Norway Grants
ID : Pol-Nor/209260/108/2015
Références
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pubmed: 7962581