## 4 Results and Discussion
To summarize, conventional models for document/sentence-level sentiment analysis, like VADER and DistilFinRoBERTa, proved inadequate for TLFSA, even if the latter is trained on financial text. This highlights the limitations of traditional sentiment analysis tools in handling the nuanced and context-specific requirements of target-level sentiment analysis, especially in specialized domains like finance. The pre-trained DeBERTa-v3-base-ABSA-v1.1, despite being built for the considered task, exhibits limitations when applied to the financial domain, which is likely due to the different domain of its training data. This finding underscores the importance of domain adaptation in achieving optimal performance. Fine-tuning DeBERTa-v3-base-ABSA-v1.1 on financial data significantly enhances its efficacy, as evidenced by the high performance achieved through 5-fold cross-validation. The success of this fine-tuning process illustrates the critical role of tailored training for domain-specific applications. This result aligns with prior research suggesting that pre-trained models require domain-specific adaptation to handle specialized vocabulary, semantics, and contextual relationships effectively. LLMs such as ChatGPT-4, ChatGPT-4.0, and Gemini 1.5 Pro demonstrate substantial potential for the task. Despite not being explicitly fine-tuned on financial sentiment data, these models demonstrate performance levels comparable to those of fine-tuned DeBERTa-v3-base-ABSA-v1.1. This seems to suggest that LLMs have an inherent ability to generalize across tasks and domains, which is likely due to their extensive pre-training on diverse and large-scale datasets. Their capacity to perform well in TLFSA without additional training makes them highly versatile and efficient alternatives for direct application in such contexts, reducing the need for resource-intensive manual annotation and fine-tuning processes. These outcomes are in line with the findings observed in different contexts and experiments, such as [7; 48; 49]. The current study on TLFSA reveals notable progress in sentiment analysis in the financial sector, specifically with the fine-tuned DeBERTa-v3-base-ABSA-v1.1 and LLMs like ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro. These models exhibit very high effectiveness in capturing nuanced emotions, making them valuable instruments for businesses dependent on sentiment analysis for strategic decision making. These models can be incorporated into financial reporting and predictive systems through user-friendly APIs, allowing seamless integration with existing financial software. For financial technology firms, the integration of these models could result in more advanced financial reporting tools that combine sentiment data elicited from news headlines with other economic indicators--such as stock prices, trading volumes, interest rates, and macroeconomic trends--offering more comprehensive market predictions and supporting informed decision making. The versatility of LLMs like ChatGPT-4, which do not require extensive fine tuning for financial data, reduces implementation time and costs, making them an appealing choice for both large institutions and smaller financial firms. We recognize that integrating these models into real-world applications poses several challenges. A key issue is the interpretability of model outputs, as LLMs function as black boxes, generating outputs without transparent reasoning processes. This is particularly critical in financial contexts, where professionals require clear and justifiable explanations for sentiment predictions. Notably, ongoing research in Explainable AI [77] aims to address this challenge. Additionally, the LLM models used in our work are maintained and controlled by external entities (e.g., OpenAI, Google), which frequently update them to enhance their capabilities. This dynamic necessitates regular performance evaluations to ensure their efficacy remains consistent.