BIG5-TPoT: Predicting BIG Five Personality Traits, Facets, and Items Through Targeted Preselection of Texts
This work addresses the problem of efficient personality prediction from text for researchers and practitioners, but it is incremental as it builds on existing deep learning methods with a filtering enhancement.
The paper tackles the challenge of predicting personality traits from large text volumes by introducing a targeted preselection of texts (TPoT) strategy, which improves prediction accuracy and reduces Mean Absolute Error on the Stream of Consciousness Essays dataset.
Predicting an individual's personalities from their generated texts is a challenging task, especially when the text volume is large. In this paper, we introduce a straightforward yet effective novel strategy called targeted preselection of texts (TPoT). This method semantically filters the texts as input to a deep learning model, specifically designed to predict a Big Five personality trait, facet, or item, referred to as the BIG5-TPoT model. By selecting texts that are semantically relevant to a particular trait, facet, or item, this strategy not only addresses the issue of input text limits in large language models but also improves the Mean Absolute Error and accuracy metrics in predictions for the Stream of Consciousness Essays dataset.