The study combined - for the first time - two established ways of detecting user emotions: keystroke dynamics and text-pattern analysis.
To provide data for the study, volunteers were asked to note their emotional state after typing passages of fixed text, as well as at regular intervals during their regular ('free text') computer use.
This provided the researchers from Bangladesh with data about keystroke attributes associated with seven emotional states (joy, fear, anger, sadness, disgust, shame and guilt).
After running a variety of tests, the researchers found that their new 'combined' results were better than their separate results; what's more, the 'combined' approach improved performance for five of the seven categories of emotion.
Joy (87 per cent) and anger (81 per cent) had the highest rates of accuracy.
"Computer systems that can detect user emotion can do a lot better than the present systems in gaming, online teaching, text processing, video and image processing, user authentication and so many other areas where user emotional state is crucial, researchers said.
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