<![CDATA[The growth of public aspirations conveyed through digital media generates large and diverse textual data, requiring methods capable of processing and determining priorities objectively. This study develops a Multicriteria Decision Support model based on Natural Language Processing (NLP) to analyze and prioritize public aspirations. The research stages include aspiration data collection, text preprocessing, feature extraction, topic or category classification, and criterion weighting using the Multi-Criteria Decision Making (MCDM) method. Subsequently, the results of NLP analysis are integrated with a ranking mechanism to determine the priority level of aspirations based on urgency, impact, number of supporters, and issue relevance. The model is evaluated using classification metrics and consistency analysis of ranking results. The findings are expected to support decision-making that is faster, more objective, transparent, and data-driven.]]>
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