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 Eugene Sy, Tzu-Cheng Peng, Shih-Hsuan Huang, Hen-You Lin, and Yung-Chun Chang Graduate Institute of Data Science, Taipei Medical University, Taiwan {m946111012, m946111003, m946110008, m946111008, changyc}@tmu.edu.tw Abstract 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. Keywords: Financial NLP, Ensemble Learning, Sentiment Analysis 1 Introduction Financial technology (Fintech) has been developing for several years, and among them, Natural Language Processing (NLP) has gradually become a pivotal tool driving financial text analysis. Financial text analysis entails thorough examination of voluminous textual information within the financial domain, with the objective of unveiling the embedded emotions, sentiments, and logical frameworks, thereby offering supportive advice to investors and decision-makers. By taking advantage of natural language processing techniques, conducting sentiment analysis on financial texts effectively captures the emotional nuances within the content, subsequently predicting market trends and gaining insights into the oscillations of market participants' sentiments (Gupta, R., & Chen, M., 2020). Additionally, the application of natural language processing extends to natural language inference, dissecting the logical relationships among sentences in financial texts to uncover the structures of arguments and inferences, facilitating a profound comprehension of the underlying viewpoints and perspectives within the text (Chu et al, 2022). The amalgamation of these techniques not only enhances the precision and efficiency of financial text analysis but also provides an abounding source of information for decision-makers in the financial world, aiding them in making well- informed choices. The textual data in the financial domain is vast and diverse, spanning various types such as analyst reports, earnings conference calls, and social media discussions. These texts not only encompass a wealth of information capable of influencing market sentiment and the formulation of investment strategies but also complicate and lengthen the process of effectively processing and analyzing this data. It is noteworthy that financial texts are replete with intricate technical terminology, posing a heightened level of challenge for natural language processing technology. Consequently, the specialized terminology within the financial domain presents hurdles for achieving the precision and efficacy of sentiment analysis and natural language inference, necessitating a more refined and intricate approach. Emotions within financial The 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023) Taipei City, Taiwan, October 20-21, 2023. The Association for Computational Linguistics and Chinese Language Processing texts encompass a multitude of sentiments, ranging from positive and negative to neutral emotions, uncertainty, and emotional fluctuations. This wide spectrum of emotions significantly …

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