Affective computing
Keystroke analysis of free text
ACM Transactions on Information and System Security (TISSEC)
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
User Modeling and User-Adapted Interaction
Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction
Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction
Affective Computing: From Laughter to IEEE
IEEE Transactions on Affective Computing
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Identifying emotional states using keystroke dynamics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Emotions during writing on topics that align or misalign with personal beliefs
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Context-Sensitive Learning for Enhanced Audiovisual Emotion Classification
IEEE Transactions on Affective Computing
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It is hypothesized that the ability for a system to automatically detect and respond to users' affective states can greatly enhance the human-computer interaction experience. Although there are currently many options for affect detection, keystroke analysis offers several attractive advantages to traditional methods. In this paper, we consider the possibility of automatically discriminating between natural occurrences of boredom, engagement, and neutral by analyzing keystrokes, task appraisals, and stable traits of 44 individuals engaged in a writing task. The analyses explored several different arrangements of the data: using downsampled and/or standardized data; distinguishing between three different affect states or groups of two; and using keystroke/timing features in isolation or coupled with stable traits and/or task appraisals. The results indicated that the use of raw data and the feature set that combined keystroke/timing features with task appraisals and stable traits, yielded accuracies that were 11% to 38% above random guessing and generalized to new individuals. Applications of our affect detector for intelligent interfaces that provide engagement support during writing are discussed.