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Keystroke Dynamics Patterns While Writing Positive and Negative Opinions
Agata Kołakowska1, Agnieszka Landowska1
1Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland.
Sensors (Basel, Switzerland)
|September 10, 2021
Summary
Analyzing human-computer interaction, this study found keystroke dynamics can detect positive and negative opinions. While accuracy was modest, combining this with other data shows promise for sentiment analysis.
Area of Science:
- Human-Computer Interaction
- Psychology
- Computer Science
Background:
- Understanding user emotions and opinions during digital interactions is crucial.
- Keystroke dynamics offer a non-intrusive method for analyzing user behavior.
- Previous research has explored physiological signals for emotion detection, but keystroke patterns remain less explored for opinion classification.
Purpose of the Study:
- To analyze behavioral patterns in human-computer interaction by examining keystroke dynamics.
- To determine if keystroke dynamics can differentiate between positive and negative opinions expressed in text.
- To compare the effectiveness of keystroke dynamics versus self-reported emotional states (pleasure and arousal) in opinion classification.
Main Methods:
- A semi-experiment involving 50 participants recalling and writing about positive and negative learning experiences.
- Capturing detailed keystroke dynamics (e.g., timing, pressure, speed) during opinion writing.
- Calculating over 50 diverse features from keystroke data for analysis and classification.
- Employing machine learning models for opinion classification based on keystroke features and self-report labels.
Main Results:
- Keystroke dynamics alone achieved accuracy slightly above random chance in differentiating positive and negative opinions.
- Classification using self-reported pleasure and arousal labels demonstrated significantly more accurate results.
- The study confirmed a detectable correlation between keystroke patterns and opinion sentiment, albeit with limitations.
Conclusions:
- Keystroke dynamics show potential for recognizing sentiment in human-computer interaction, exceeding random guessing.
- Integrating keystroke data with other modalities (e.g., self-reports, facial expressions) is recommended for enhanced accuracy.
- Further research into multimodal approaches can improve the reliability of automated opinion and emotion detection.
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