<![CDATA[This research delineates the conceptual advancement of a sentiment analysis model employing Large Language Models (LLMs), augmented by a dynamic weighting system predicated on the strategic significance of product attributes. This research, based on a systematic review of seven recent studies predominantly utilizing conventional NLP methodologies discovers significant deficiencies, such as disjointed sentiment extraction and the absence of contextual, strategic weighting. Prior studies have established the efficacy of Natural Language Processing (NLP) techniques in evaluating customer satisfaction and online reviews; however, there has been a scarcity of initiatives that integrate sentiment analysis with product prioritization in decision-making processes. The suggested framework presents an innovative amalgamation of LLM-based sentiment analysis with a strategic weighting system that adapts in real-time according to business priorities, setting it apart from earlier customer analytics frameworks that consider sentiment and strategy in isolation. To conceptually validate this model, a thematic synthesis and comparative mapping approach were employed to assess the potential of the proposed components to enhance interpretability and alignment between customer feedback and product decisions. Initial conceptual analysis indicates that the framework may improve decision quality by integrating profound contextual sentiment insights with flexible business prioritization. The goal is to improve strategies for making products better, make sure that customer feedback is in line with strategic goals, and help businesses make decisions based on data in changing business environments.]]>
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