Predicting Perceived Emotions in Animated GIFs with 3D Convolutional Neural Networks
Author(s)
Picard, Rosalind W.; Chen, Weixuan
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© 2016 IEEE. Animated GIFs are widely used on the Internet to express emotions, but their automatic analysis is largely unexplored before. To help with the search and recommendation of GIFs, we aim to predict their emotions perceived by humans based on their contents. Since previous solutions to this problem only utilize image-based features and lose all the motion information, we propose to use 3D convolutional neural networks (CNNs) to extract spatiotemporal features from GIFs. We evaluate our methodology on a crowd-sourcing platform called GIFGIF with more than 6000 animated GIFs, and achieve a better accuracy then any previous approach in predicting crowd-sourced intensity scores of 17 emotions. It is also found that our trained model can be used to distinguish and cluster emotions in terms of valence and risk perception.
Date issued
2016-12Department
Massachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chen, Weixuan and Picard, Rosalind W. 2016. "Predicting Perceived Emotions in Animated GIFs with 3D Convolutional Neural Networks."
Version: Author's final manuscript