Detecting Negative Emotion for Mixed Initiative Visual Analytics

Contributors

Prateek Panwar, Christopher Collins.

Abstract

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 a discussing the best performing model, Random Forest, and its future applications for providing just-in-time assistance for visual analytics.

Publications

• 
  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.
  [Bibtex] [PDF]

  @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.
  [Bibtex] [PDF]

  @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
  }