Fine-Grained Argument Understanding with BERT Ensemble Techniques: A Deep Dive into Financial Sentiment Analysis

Fine-Grained Argument Understanding with BERT Ensemble Techniques: A Deep Dive into Financial Sentiment Analysis

While argument mining has garnered attention over the years, its application in the financial sector remains nascent. This study presents a BERT-based ensemble learning approach tailored for sentiment analysis grounded in financial narratives, specifically focusing on unearthing arguments. For a nuanced analysis, we dissect the challenge into two pivotal subtasks using earnings conference call data: (1) Argument Unit Classification, and (2) Argument Relation Detection and Classification. Experimental results substantiate that our approach not only effectively forecasts both tasks but also outperforms the comparisons and achieve SOTA performance. This underscores the potential of our method in fine-grained argument understanding within financial analysis.