用LSTM分析7733条印尼营养餐政策评论，准确率达89%。
Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method
- 采用LSTM模型分析YouTube评论情感倾向
- 整体准确率89%，负面情绪识别F1达0.94
- 适合关注社交媒体舆情与政策评估的研究者
公众对YouTube社交媒体上免费营养餐计划（MBG）的舆论反映多样。本研究应用长短期记忆（LSTM）方法对7,733条YouTube评论进行情感分类。结果表明，LSTM模型达到89%的准确率，对负面情绪识别表现优异（F1-score 0.94），但正面情绪识别较弱（F1-score 0.55），主要因负面数据占数据集87.7%，存在显著类别不平衡问题。研究证实LSTM在印尼语文本情感分析中的有效性，同时揭示了数据不平衡带来的挑战。该工作为基于社交媒体的公共政策评估提供了方法支持。
原文摘要 · Abstract (English)
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
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