A Sentiment Analysis On Youtube Comments On Sumatera Flash Flood Disaster Videos Using The Long Short-Term Memory (LSTM) Method

Abstract

The flash flood disaster in Sumatra generated various public responses, many of which were expressed through comments on the YouTube platform. These comments contain opinions, criticism, support, and emotional expressions that can be analyzed to identify public sentiment trends regarding the disaster. This study aims to analyze the sentiment of YouTube comments related to flash flood disaster videos in Sumatra using the Long Short-Term Memory (LSTM) method. The research data were collected through the YouTube Data API v3, resulting in 6,097 Indonesian-language comments. The research stages included data collection, preprocessing consisting of cleaning, case folding, word normalization, tokenization, stopword removal, and stemming, sentiment labeling using a lexicon-based approach, data splitting with a ratio of 80% training data and 20% testing data, and sentiment classification using an LSTM model. The labeling results showed that negative sentiment dominated with 2,234 comments (37.69%), followed by positive sentiment with 2,011 comments (33.93%), and neutral sentiment with 1,682 comments (28.38%). Based on the evaluation results, the LSTM model achieved an accuracy of 77.64%, precision of 78.26%, recall of 77.55%, and an F1-score of 77.45%. The findings indicate that the Long Short-Term Memory (LSTM) method is capable of classifying YouTube comment sentiments effectively and can be utilized to represent public opinion regarding flash flood disasters in an automated and objective manner.