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Large language models (LLMs) are increasingly vital across diverse applications. This study delves into the utilization of the Alpaca language model, a cost-effective and high-performing variant, for sentiment analysis. The research systematically evaluates the Alpaca model's performance across a range of datasets, with an emphasis on its ability to excel in sentiment analysis tasks across varying domains. The study sheds light on the practical utility of the Alpaca model in sentiment analysis applications, providing insights into its adaptability and effectiveness. Furthermore, this investigation extends its inquiry into the realm of consumer-grade graphics, examining how large language models perform in sentiment analysis within this context. Notably, the study uncovers that these models demonstrate proficiency in nuanced tasks with limited answer options. Moreover, the research underscores the potential for improved accuracy through the utilization of larger training datasets. In conclusion, this research showcases the Alpaca model's efficacy in sentiment analysis and advances our understanding of LLMs capabilities in this field. It underscores their potential to enhance context-aware and precise sentiment analysis solutions, offering valuable contributions to the broader landscape of natural language processing.
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