Personality trait detection via transfer learning

Abstract

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains, including education, e-commerce, or human resources. Traditional machine learning techniques have been broadly employed for personality trait identification; nevertheless, the development of new technologies based on deep learning has led to new opportunities to improve their performance. This study focuses on the capabilities of pre-Trained language models such as BERT, RoBERTa, ALBERT, ELECTRA, ERNIE, or XLNet, to deal with the task of personality recognition. These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-Term dependencies. This makes them suitable to classify multilabel personality traits from reviews while mitigating computational costs. The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover, it is able to fine-Tune the previous models using theMyPersonality dataset, which comprises 9,917 status updates contributed by 250 Facebook users. These status updates are categorized according to the well-known Big Five personality model, setting the stage for a comprehensive exploration of personality traits. To test the proposal, a set of experiments have been performed using different metrics such as the exact match ratio, hamming loss, zero-one-loss, precision, recall, F1-score, andweighted averages. The results reveal ERNIE is the topperforming model, achieving an exact match ratio of 72.32%, an accuracy rate of 87.17%, and 84.41% of F1-score. The findings demonstrate that the testedmodels substantially outperform other state-of-The-Art studies, enhancing the accuracy by at least 3% and confirming them as powerful tools for personality recognition. These findings represent substantial advancements in personality recognition, making them appropriate for the development of user-centric applications.