Recent trends in deep learning based personality detection
作者:Yash Mehta, Navonil Majumder, Alexander Gelbukh, Erik Cambria
摘要
Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.
论文关键词:Personality detection, Multimodal interaction, Deep learning
论文评审过程:
论文官网地址:https://doi.org/10.1007/s10462-019-09770-z