<![CDATA[This study presents the conceptual development of a sentiment analysis model using Large Language Models (LLMs), integrated with a dynamic weighting system based on the strategic value of product attributes. While previous research has demonstrated the effectiveness of Natural Language Processing (NLP) techniques in analyzing customer satisfaction and online reviews, few efforts have aligned sentiment insights with product prioritization in decision-making. Drawing from a systematic review of seven recent studies—most of which rely on traditional NLP approaches—this research identifies critical gaps, including fragmented sentiment extraction and the lack of contextual, strategic weighting. The proposed LLM-based framework advances prior insights by combining sentiment analysis, customer voice modeling, and adaptive prioritization mechanisms. The outcome is intended to enhance product improvement strategies, align customer feedback with strategic objectives, and support data-driven decision-making in dynamic business environments.]]>
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