Novel Curriculum Learning Strategy Using Class-Based TF-IDF for Enhancing Personality Detection in Text

Personality detection plays a pivotal role in social interactions, machine learning (ML), and natural language processing (NLP). Its goal is to discern an individual's traits from their behavior and expressions. The prevalence of text-based communication has sparked interest in inferring personality from written content. However, challenges persist in accurately interpreting traits like the Big-Five or Myers-Briggs Type Indicator. These challenges stem from the reliance on self-reported surveys for labeling, which introduces uncertainties as individual assessments may not consistently align with their actual personality. In this paper, we propose novel curriculum strategies that employ class-based term frequency-inverse document frequency (c-TF-IDF) to enhance personality detection performance. By leveraging a curriculum approach that mirrors human learning progression, starting from simpler tasks and moving toward more complex ones, these strategies aim to train models on progressively challenging scenarios. Our experimental results demonstrate that these proposed curriculum-based strategies improve the accuracy of personality detection compared to previously suggested methods. This study contributes to advance understanding of text-based cues for personality inference. It has the potential to enrich various fields, including human-computer interaction, personalized recommendations, and targeted marketing.