Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language

TL;DR

This work investigates the identification of Charis-matic Leadership Tactics in natural language using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, offering potential methods to simplify the currently elaborate assessment of charisma in texts.

Abstract

This work investigates the identification of Charismatic Leadership Tactics (CLTs) in natural language using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. Based on an own extensive corpus of CLTs generated and curated for this task, our methodology entails training a machine learning model that is capable of accurately identifying the presence of these tactics in natural language. A performance evaluation is conducted to assess the effectiveness of our model in detecting CLTs. We find that the total accuracy over the detection of all CLTs is 98.96\% The results of this study have significant implications for research in psychology and management, offering potential methods to simplify the currently elaborate assessment of charisma in texts.

Figures (1)

• Figure 1: Confusion matrix for the charismatic leadership tactics multilabel classification with BERT.