# Recent Trends in Deep Learning Based Personality Detection

## TL;DR

This paper reviews recent advances in deep learning methods for automated personality detection using multimodal data, highlighting models, datasets, and applications in affective computing.

## Contribution

It provides a comprehensive overview of deep learning-based approaches for personality detection, emphasizing multimodal methods and recent technological trends.

## Key findings

- Deep learning models have significantly advanced personality detection accuracy.
- Multimodal data integration improves prediction performance.
- The survey highlights key datasets and industrial applications.

## Abstract

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.