Abstract:Public opinion towards the Free Nutritious Meal Program (MBG) on YouTube social media reflects diverse community responses. This study applies the Long Short-Term Memory (LSTM) method to classify sentiments from 7,733 YouTube comments. The results show that the LSTM model achieves 89% accuracy, with strong performance on negative sentiment (F1-score 0.94) but weaker performance on positive sentiment (F1-score 0.55) due to class imbalance, as negative data account for 87.7% of the dataset. These findings confirm the effectiveness of LSTM for sentiment analysis of Indonesian text while highlighting the challenge of imbalanced data. This research contributes to social media-based public policy evaluation
| Comments: | 10 pages 3 figures 3 tables Conference submission on YouTube sentiment classification using LSTM for the Free Nutritious Meal Program |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2604.26312 [cs.CL] |
| (or arXiv:2604.26312v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26312 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Martin Clinton Tosima Manullang [view email]
[v1]
Wed, 29 Apr 2026 05:38:49 UTC (192 KB)