Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications
It addresses sentiment analysis for financial professionals, but is incremental as it applies existing methods to a specific domain.
This study compared deep learning models like BERT, FinBERT, and ULMFiT for sentiment analysis of earnings call transcripts to aid investment decisions, evaluating their performance with metrics such as accuracy and F1-score.
This study presents a comparative analysis of deep learning methodologies such as BERT, FinBERT and ULMFiT for sentiment analysis of earnings call transcripts. The objective is to investigate how Natural Language Processing (NLP) can be leveraged to extract sentiment from large-scale financial transcripts, thereby aiding in more informed investment decisions and risk management strategies. We examine the strengths and limitations of each model in the context of financial sentiment analysis, focusing on data preprocessing requirements, computational efficiency, and model optimization. Through rigorous experimentation, we evaluate their performance using key metrics, including accuracy, precision, recall, and F1-score. Furthermore, we discuss potential enhancements to improve the effectiveness of these models in financial text analysis, providing insights into their applicability for real-world financial decision-making.