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DLP-personality detection: a text-based personality detection framework with psycholinguistic features and pre-trained features

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Abstract

Text-based personality detection aims to identify the personality traits implied in subject-supplied textual data. However, over-reliance on pre-trained language models and neglect of psycholinguistic features has become a bottleneck in personality detection. In this work, we conduct extensive feature-level ablation experiments using multiple psycholinguistic features to verify the importance of psycholinguistic features for personality detection. Furthermore, we propose a novel personality detection framework, DLP-Personality Detection, which fuses multiple psycholinguistic features and pre-trained language features. With the DLP-Personality Detection, we achieve state-of-the-art performance for the Big Five personality traits (Big 5) and Myers-Briggs Type Indicator (MBTI) personality traits on the Essays and Kaggle MBTI datasets. Finally, we provide some suggestions for psycholinguistic features and discuss future work for personality detection.

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Data Availability

The data that support the findings of this study are available from https://github.com/ml-papers-coders/Keras-BigFive-personality-traits/blog/ and https://www.kaggle.com/datasets/datanaek/mbit-type.

Notes

  1. pypi.org/project/readability/

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  1. Tianjin University of Technology, School of Computer Science and Engineering, Tianjin, China

    Hao Lin

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Lin, H. DLP-personality detection: a text-based personality detection framework with psycholinguistic features and pre-trained features. Multimed Tools Appl 83, 37275–37294 (2024). https://doi.org/10.1007/s11042-023-17015-z

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