Public Sentiment Analysis of Free Nutritious Meal Program Discourse on Social Media X Using Support Vector Machine N-Gram Features Based
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Abstract
The Free Nutritious Meal Program is a government policy aimed at improving the nutritional quality of society and has generated diverse public responses on social media. This study aims to analyze public sentiment toward the Free Nutritious Meal Program on social media X using the Support Vector Machine (SVM) algorithm with N-Gram features and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The data were collected through a crawling process from social media X, resulting in 1,014 tweets. After data cleaning, 931 tweets were obtained and labeled into two sentiment classes, namely positive and negative. The research stages include text preprocessing, N-Gram feature extraction (unigram and bigram), classification using the SVM algorithm, and model evaluation using the 10-Fold Cross-Validation method with the assistance of the RapidMiner tool. The experimental results show that the SVM model achieved an accuracy of 79.59%. Although the precision value for the negative class is relatively high, the recall and F1-score remain relatively low due to the imbalance in data distribution. Overall, the results indicate that public sentiment toward the Free Nutritious Meal Program on social media X is dominated by positive sentiment. The findings of this study are expected to serve as an initial evaluation for the government in understanding public perceptions of the implementation of the program.
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