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Predicting Emotions From Multimodal Users' Data

Published: 03 July 2018 Publication History

Abstract

Prediction of emotions is important for understanding human be-havior and modeling users in learning environments. In this paper,we present a deep multi-modal architecture for emotions predic-tion, which takes advantage of deep learning, user multimodal dataand the hierarchy of human memory. The architecture consists ofthe combination of Long Short-Term memory (LSTMs). One of thenovelty of our approach is that, we enhance the LSTM with anexplicit memory since in brain studies, the memory is often dividedinto two further main types: explicit (or declarative) memory andimplicit (or procedural) memory, the last one being the main pur-pose of LSTMs architectures. The resulting model has been testedon a public multi-modal dataset.

References

[1]
Richard C Atkinson and Richard M Shiffrin. 1968. Human memory: A proposed system and its control processes1. In Psychology of learning and motivation. Vol. 2. Elsevier, 89--195.
[2]
Paul Ekman,Wallace V Friesen, and Phoebe Ellsworth. 2013. Emotion in the human face: Guidelines for research and an integration of findings. Elsevier.
[3]
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11). 689--696.
[4]
Ange Tato, Roger Nkambou, Aude Dufresne, and Miriam H Beauchamp. 2017. Convolutional Neural Network for Automatic Detection of Sociomoral Reasoning Level. In Proceedings of the 10th International Conference on Educational Data Mining (EDM). 284--289.

Cited By

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  • (2022)Computational Modeling of Bilingual Language Learning: Current Models and Future DirectionsLanguage Learning10.1111/lang.1252973:S2(17-64)Online publication date: 31-Oct-2022
  • (2020)Personality Prediction of Social Network Users Using Ensemble and XGBoostProgress in Computing, Analytics and Networking10.1007/978-981-15-2414-1_14(133-140)Online publication date: 27-Mar-2020
  • (2019)Using textual data for Personality Prediction:A Machine Learning Approach2019 4th International Conference on Information Systems and Computer Networks (ISCON)10.1109/ISCON47742.2019.9036220(529-533)Online publication date: Nov-2019

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Published In

cover image ACM Conferences
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
July 2018
393 pages
ISBN:9781450355896
DOI:10.1145/3209219
  • General Chairs:
  • Tanja Mitrovic,
  • Jie Zhang,
  • Program Chairs:
  • Li Chen,
  • David Chin
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2018

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Author Tags

  1. affective forecasts
  2. emotions prediction
  3. long term memory
  4. multi-modal deep learning
  5. user modeling

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  • Extended-abstract

Funding Sources

  • Beam Me Up Inc.
  • Natural Sciences and EngineeringResearch Council of Canada Discovery Grant Program

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UMAP '18
Sponsor:

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UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

View all
  • (2022)Computational Modeling of Bilingual Language Learning: Current Models and Future DirectionsLanguage Learning10.1111/lang.1252973:S2(17-64)Online publication date: 31-Oct-2022
  • (2020)Personality Prediction of Social Network Users Using Ensemble and XGBoostProgress in Computing, Analytics and Networking10.1007/978-981-15-2414-1_14(133-140)Online publication date: 27-Mar-2020
  • (2019)Using textual data for Personality Prediction:A Machine Learning Approach2019 4th International Conference on Information Systems and Computer Networks (ISCON)10.1109/ISCON47742.2019.9036220(529-533)Online publication date: Nov-2019

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