Augmented Language Dataset for Enhanced Personality Profiling

Abstract

The lexical hypothesis asserts that language encompasses all meaningful individual differences in personality. Language is a vital tool for communication and self-expression, making it essential for understanding and assessing human personality. This paper investigates personality recognition from language use, emphasizing the significance of language in capturing and analyzing personality traits. A comprehensive literature review examines various approaches and techniques in personality recognition. We investigate the effectiveness of language use in predicting personality traits, employing multiple feature extraction and data augmentation techniques to enhance the accuracy and robustness of the personality recognition models. Our approach involves training a generative model, PersonaG, on the Essays dataset, subsequently using it to generate augmented data (AUG-Essays). We compare the performance of machine learning classifiers using LIWC, TF-IDF, Glove, and Word-Vec features on both Essays and AUG-Essays datasets. Our findings demonstrate significant improvements in predictive performance, offering valuable insights for applications in human resources, marketing, and beyond.