<![CDATA[This study aims to examine the application of fine-tuned models in news sentiment analysis through the Systematic Literature Review (SLR) approach. The main focus is directed at three aspects: improving accuracy (RQ1), implementation challenges (RQ2), and computational efficiency (RQ3). The problems identified include high computational requirements, limited annotated data, and difficulties in handling language and dialect diversity. As a solution, various optimization techniques have been explored, such as domain-specific fine-tuning, knowledge distillation, quantization, and hybrid approaches that combine fine-tuned models with lexical methods. The results of the review show that fine-tuned models, especially BERT, are capable of significantly improving sentiment analysis accuracy compared to traditional machine learning models, although they still face limitations in terms of efficiency and scalability. This study provides an important foundation for the development of more accurate, efficient, and applicable models in real-world scenarios, including news media monitoring and automated content moderation systems.]]>
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