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LSTM model achieves 89% accuracy classifying YouTube comments on meal program

A study utilized the Long Short-Term Memory (LSTM) method to analyze public opinion on Indonesia's Free Nutritional Meal Program using 7,733 YouTube comments. The LSTM model achieved 89% accuracy in classifying sentiments, demonstrating strong performance for negative comments but facing challenges with positive sentiment due to data imbalance. This research highlights LSTM's effectiveness for Indonesian text sentiment analysis and its contribution to evaluating public policy through social media. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Demonstrates LSTM's utility for social media sentiment analysis in public policy evaluation.

RANK_REASON Academic paper on applying an LSTM model for sentiment analysis of social media comments.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Berliana Enda Putri, Lisa Diani Amelia, Muhammad Zaky Zaiddan, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang ·

    Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method

    arXiv:2604.26312v1 Announce Type: new 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 commen…

  2. arXiv cs.CL TIER_1 · Martin Clinton Tosima Manullang ·

    Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method

    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 achieve…