Text-based automatic personality prediction: A bibliographic review

TL;DR

This paper reviews NLP-based automatic personality prediction methods since 2010, categorizing approaches into pre-trained independent, pre-trained model-based, and multimodal, highlighting recent deep learning advancements and dataset evaluations.

Contribution

It provides a comprehensive overview of NLP approaches for APP, categorizing methods and comparing results based on datasets, with a focus on recent deep learning techniques.

Findings

Deep learning and transfer learning have advanced APP research.

Pre-trained models outperform earlier methods in accuracy.

Multimodal approaches show promise for improved predictions.

Abstract

Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three; pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets.