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Cui-Xia Ma, Jian-Cheng Song, Qian Zhu, Kevin Maher, Ze-Yuan Huang, Hong-An Wang. EmotionMap: Visual Analysis of Video Emotional Content on a Map[J]. Journal of Computer Science and Technology, 2020, 35(3): 576-591. DOI: 10.1007/s11390-020-0271-2
Citation: Cui-Xia Ma, Jian-Cheng Song, Qian Zhu, Kevin Maher, Ze-Yuan Huang, Hong-An Wang. EmotionMap: Visual Analysis of Video Emotional Content on a Map[J]. Journal of Computer Science and Technology, 2020, 35(3): 576-591. DOI: 10.1007/s11390-020-0271-2

EmotionMap: Visual Analysis of Video Emotional Content on a Map

  • Emotion plays a crucial role in gratifying users’ needs during their experience of movies and TV series, and may be underutilized as a framework for exploring video content and analysis. In this paper, we present EmotionMap, a novel way of presenting emotion for daily users in 2D geography, fusing spatio-temporal information with emotional data. The interface is composed of novel visualization elements interconnected to facilitate video content exploration, understanding, and searching. EmotionMap allows understanding of the overall emotion at a glance while also giving a rapid understanding of the details. Firstly, we develop EmotionDisc which is an effective tool for collecting audiences’ emotion based on emotion representation models. We collect audience and character emotional data, and then integrate the metaphor of a map to visualize video content and emotion in a hierarchical structure. EmotionMap combines sketch interaction, providing a natural approach for users’ active exploration. The novelty and the effectiveness of EmotionMap have been demonstrated by the user study and experts’ feedback.
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