The master’s thesis explores the application of large language models in the sentiment analysis of financial news. It emphasizes the shift from traditional lexicon-based and rule-driven approaches to advanced neural architectures capable of contextual understanding and semantic interpretation. The theoretical part presents the transformer architecture, which enables efficient processing of sequential data through the self-attention mechanism. Key models such as BERT and GPT, along with domain-specific adaptations like FinBERT, are discussed. The empirical part involves sentiment analysis of financial news using one classical model and three large language models. The predicted sentiment is compared with movements in financial indices to examine the relationship between news sentiment and market dynamics. The results show that large language models sometimes outperform traditional methods in contextual comprehension and sentiment detection, demonstrating their potential for automated interpretation of market information and enhanced decision-making in finance.
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