Our paper "Improving Sentiment Analysis with Biofeedback Data" was recently published.
In this cooperative work between the Data Science chair and the chair of Human-Computer-Interaction, we present an approach of incorporating biofeedback features derived from heart rate and brain waves signals. The biofeedback data was recorded from “Humans-in-the-Loop” to improve machine learning models on the tasks of sentiment analysis and sentiment detection.
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