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. Late-breaking Work.
    [Bibtex] [PDF]
    @poster{pan2018a,
    author = {Prateek Panwar and Christopher Collins},
    title = {Detecting Negative Emotion for Mixed Initiative Visual Analytics},
    venue = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)},
    year = 2018,
    note = {Late-breaking Work}
    }

Research

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop

PivotSlice

Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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