ESTG - Mestrado em Ciência de Dados
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Browsing ESTG - Mestrado em Ciência de Dados by Subject "Análise de sentimento financeiro"
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- The Specialist vs. The Generalist: A Comparative Analysis of Performance and Explainability for Financial Sentiment ClassificationPublication . Roque, Miguel Augusto; Miragaia, Rolando Lúcio Germano; Grilo, Carlos Fernando de AlmeidaThe accurate and transparent classification of sentiment in financial texts is a cornerstone of computational finance. This field is currently at a methodological crossroads, dominated by two paradigms: the fine-tuned specialist, represented by domain-adapted models like FinBERT, and the instructed generalist, embodied by modern Large Language Models (LLMs) like Google's Gemini. While performance benchmarks are emerging, a significant research gap exists in the systematic comparison of their performance trade-offs and the nature of their explainability. This dissertation conducts a comparative study between a fine-tuned FinBERT model and the Gemini 2.5 Pro LLM on an extended version of the Financial PhraseBank dataset. The analysis is performed along two axes: (1) Classification Performance, evaluated via metrics robust to class imbalance, and (2) Explainability, where FinBERT's predictions are analyzed using SHapley Additive exPlanations (SHAP). For Gemini, two distinct prompting protocols are compared: a two-step Separated Protocol designed to rigorously test the "overthinking" hypothesis and a single-step Simultaneous Protocol. The results reveal a nuanced performance verdict. While FinBERT excels in accuracy, a key finding is that both Gemini protocols achieve virtually identical performance, challenging the initial "overthinking" hypothesis and suggesting a high degree of robustness in modern LLMs. The qualitative analysis uncovers two distinct reasoning styles: FinBERT's logic is bottom-up and pattern-based, excelling at domain-specific jargon, while Gemini's is top-down and conceptual, grasping holistic meaning but failing on specialized idioms. Ultimately, this work concludes that the choice between a specialist and a generalist is not one of absolute superiority, but a strategic trade-off between accuracy, risk sensitivity, implementation cost, and the desired nature of explainability. This dissertation provides a comprehensive framework for navigating that trade-off.
