Sentiment Analysis of Free Nutritious Meal Programme on Social Media X using Linear Regression and Random Forest Algorithms

Abstract

This study analyzes public sentiment towards the Free Nutritional Food Program on social media platform X using Linear Regression and Random Forest algorithms. By collecting data from Twitter and employing sentiment analysis methods based on natural language processing, this research aims to measure societal perceptions and compare the effectiveness of both algorithms in sentiment classification. The results indicate that Random Forest outperforms Linear Regression with an accuracy of 0.85 and a recall of 0.97, compared to Linear Regression, which achieves an accuracy of 0.83 and a recall of 0.91. While Linear Regression excels in precision with a score of 0.86, whereas Random Forest records 0.85, overall, Random Forest achieves a higher F1-Score of 0.90 compared to Linear Regression's score of 0.88. These findings provide important insights for governments and policymakers in responding to public opinion and designing more effective interventions to enhance the program