Contributors:
The work describes an efficient model to detect negative mind states caused by visual analytics tasks. We have developed a method for collecting data from multiple sensors, including GSR and eye-tracking, and quickly generating labelled training data for the machine learning model. Using this method we have created a dataset from 28 participants carrying out intentionally difficult visualization tasks. We have concluded the paper by discussing the best performing model, Random Forest, and its future applications for providing just-in-time assistance for visual analytics.
Publications
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P. Panwar and C. Collins, “Detecting Negative Emotion for Mixed Initiative Visual Analytics,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), 2018.
@poster{pan2018a,
author = {Prateek Panwar and Christopher Collins},
title = {Detecting Negative Emotion for Mixed Initiative Visual Analytics},
booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)},
year = 2018,
note = {Late-breaking Work}
} -
P. Panwar, A. Bradley, and C. Collins, “Detecting Negative Emotion for Mixed Initiative Visual Analytics,” in Machine Learning from User Interaction for Visualization and Analytics, 2018.
@InProceedings{pan2018b,
author = {Prateek Panwar and Adam Bradley and Christopher Collins},
title = {Detecting Negative Emotion for Mixed Initiative Visual Analytics},
booktitle = {Machine Learning from User Interaction for Visualization and Analytics},
year = 2018
}