Multitask Learning for Emotion and Personality Detection
This work addresses the problem of efficiently detecting personality traits and emotions for individuals, which is relevant for applications leveraging digital footprints.
This paper proposes SoGMTL, a multitask learning framework that simultaneously predicts personality traits and emotions. The CNN-based model achieves state-of-the-art performance on multiple personality and emotion datasets, outperforming even Language Model-based approaches.
In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual. Moreover, many researchers have demonstrated that there is a strong link between personality traits and emotions. In this paper, we build on the known correlation between personality traits and emotional behaviors, and propose a novel multitask learning framework, SoGMTL that simultaneously predicts both of them. We also empirically evaluate and discuss different information-sharing mechanisms between the two tasks. To ensure the high quality of the learning process, we adopt a MAML-like framework for model optimization. Our more computationally efficient CNN-based multitask model achieves the state-of-the-art performance across multiple famous personality and emotion datasets, even outperforming Language Model based models.