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

TL;DR

This paper presents a fine-tuned BERT model trained on a new corpus to accurately detect Charismatic Leadership Tactics in natural language, achieving high accuracy and aiding psychological and managerial assessments.

Contribution

It introduces a novel dataset and demonstrates the effectiveness of fine-tuned BERT for identifying leadership tactics in text.

Findings

Achieved 98.96% accuracy in detecting CLTs

Developed an extensive, curated corpus of CLT examples

Showed potential for simplifying charisma assessment 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.