<![CDATA[The free lunch program is one of the populist policies launched by the government with the aim of improving the nutrition of elementary and secondary school students. However, in the digital era full of community participation, the community's response to this policy is important to understand in more depth. This study aims to analyze the sentiment of social media users, especially Twitter, towards the free lunch program using the Vector Space Model (VSM) algorithm. Data were collected from 2,330 relevant tweets using keyword-based data retrieval techniques. The analysis process includes text preprocessing, feature extraction with TF-IDF, and vector similarity measurement using cosine similarity to classify sentiment into positive, negative, and neutral categories. The results of the analysis show that 63.7% of tweets are negative, 31.5% are positive, and 4.7% are neutral. Negative sentiment generally contains criticism of policy transparency, budget effectiveness, and indications of program politicization, while positive sentiment pressures the benefit program on child nutrition. These findings indicate that the VSM algorithm can be used effectively to capture public opinion patterns based on social media text. Furthermore, these results provide important meaning for policy design to consider public perception in designing and communicating social programs in a more inclusive and responsive manner.]]>
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