Application of Transformer-Based Language Models in Neuro-Linguistic Programming for Automated Personality Insights from Large-Scale Textual Corpora

In the field of Neuro-Linguistic Programming (NLP), this study investigates the implementation of transformer-based language models to automate the extraction of personality insights from extensive textual corpora. With the use of transformer architectures such as GPT-3 and cutting-edge natural language processing methods, the work presents an innovative approach for identifying and analysing complex linguistic patterns present in large text corpora. In order to enable the models to capture subtle expressions associated with different aspects of personality, the process entails fine-tuning pre-trained transformer models on multiple datasets with annotated personality attributes. The primary goal is to create a reliable automated personality analysis system that can yield precise and comprehensive insights. Significant objectives include training and optimizing transformer models to enable personality trait prediction from textual input that has not been seen before. The GPT-3.5 transformer-based language model and the Hugging Face Transformers library were used to implement automated personality analysis. The model obtained an 85% accuracy, 86% precision, 84% recall, and 85% F1-score on the social media dataset. This research contributes to the growing field of personality analysis by combining NLP techniques with transformer-based language models, creating new opportunities for creative applications in the comprehension and interpretation of human conduct.