A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks

Sree Bhattacharyya, Shuhua Yang, James Z. Wang
The Pennsylvania State University, USA
Abstract:

The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communication and understanding of human behaviors. Despite significant advancements in Affective Computing, the diverse factors influencing user emotions in social networks remain relatively understudied. Moreover, there is a notable lack of deep learning-based methods for predicting user emotions in social networks, which could be addressed by leveraging the extensive multimodal data available. This work presents a novel formulation of personalized emotion prediction in social networks based on heterogeneous graph learning. Building upon this formulation, we design HMG-Emo, a Heterogeneous Multimodal Graph Learning Framework that utilizes deep learning-based features for user emotion recognition. Additionally, we include a dynamic context fusion module in HMG-Emo that is capable of adaptively integrating the different modalities in social media data. Through extensive experiments, we demonstrate the effectiveness of HMG-Emo and verify the superiority of adopting a graph neural network-based approach, which outperforms existing baselines that use rich hand-crafted features. To the best of our knowledge, HMG-Emo is the first multimodal and deep-learning-based approach to predict personalized emotions within online social networks. Our work highlights the significance of exploiting advanced deep learning techniques for less-explored problems in Affective Computing.

Full Paper (including Appendix)
(PDF, 1.3MB)

More information


Citation: Sree Bhattacharyya, Shuhua Yang and James Z. Wang, ``A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks,'' Proceedings of the International Conference on Affective Computing and Intelligent Interaction, pp. -, Glasgow, U.K., September 2024.

© 2024 AAAC. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the AAAC.

Last Modified: August 20, 2024
© 2024