Disaster Management Sentiment Analysis Using the BiLSTM Method
DOI:
https://doi.org/10.30865/mib.v7i1.5573Keywords:
Sentiment Analysis, Twitter, BiLSTM, Word2vec, Flood Disaster, EarthquakeAbstract
Indonesia is a country prone to natural disasters. Natural disasters occur due to the process of adjustment to changes in natural conditions due to human behavior or biological processes. Community responses through tweets on Twitter are crucial for decision-making and action in disaster management and recovery processes. From the many public reactions via Twitter, sentiment analysis can be carried out. Classification using the BiLSTM method can be carried out to determine the categories of positive and negative responses after previously being compared using the SVM, which resulted in an accuracy of 82.73% and a BERT of 81.78%. After the classification process, the testing process is carried out with Word2Vec. From a total of 2,686 Twitter data, it was concluded that there were around 2,081 positive sentiments and 605 negative sentiments related to disaster management in Indonesia. At the same time, the test results obtained accuracy reached 84%, precision 88%, recall 92%, and f1-score reached 90%.References
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