BIG5-TPoT: Predicting BIG Five Personality Traits, Facets, and Items Through Targeted Preselection of Texts

TL;DR

This paper introduces BIG5-TPoT, a novel method that semantically filters texts to improve the prediction of Big Five personality traits, facets, and items from large text datasets using deep learning.

Contribution

The paper presents a targeted preselection strategy (TPoT) that enhances personality prediction accuracy by filtering relevant texts, addressing input size limitations in language models.

Findings

Improved Mean Absolute Error in personality prediction

Enhanced accuracy metrics on the Stream of Consciousness Essays dataset

Effective semantic filtering for large text inputs

Abstract

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.