Study on Text-based Personality Detection－A Review

Text-based personality detection is an important research content in the personality computing field,aiming to analyze the implicit personality traits in user-generated text.With the booming of social networks,people are accustomed to posting online content that implies their psychological activities,which provides new opportunities for text-based personality detection.Accurately detecting personality traits is important in psychological health diagnosis,public opinion monitoring,human-computer interac-tion system design,and even in the construction of large language models today.This paper provides a comprehensive review of text-based personality detection.Firstly,it introduces the background and task patterns of personality detection.Secondly,the existing detection methods are categorized into four aspects:psycholinguistic statistical methods,feature engineering me-thods,deep learning methods,and pre-trained language models.Then,the commonly used datasets and model performance are summarized.Finally,the issues and future research in this field are analyzed from five aspects:reliability,fairness,ethical and privacy,the unification of dataset and evaluation metrics,and the relationship between large language models and personality.