The Comparison of LSTM Algorithms for Twitter User Sentiment Analysis on Hospital Services During the Covid-19 Pandemic
Keywords:
Twitter, LSTM, GRU, SimpleRNN, BiLTSMAbstract
Sentiment analysis has become a crucial aspect in understanding people’s opinions and emotions on various issues. In this study, we conducted sentiment analysis on tweets related to hospital services during the COVID-19 pandemic using LSTM, BiLSTM, GRU, and SimpleRNN models. The data collection process was carried out using the Twitter API and resulted in 15,093 tweets. The data preprocessing process includes data cleaning, case folding, tokenization, filtering, and stemming. The dataset was divided into 80% for training and 20% for testing. The results showed that the BiLSTM model had the highest accuracy of 86%, followed by the GRU model with an accuracy of 86%, the LSTM model with an accuracy of 85%, and the SimpleRNN model with an accuracy of 75%. The BiLSTM model also had the highest MCC of 71%. The study concludes that the BiLSTM model outperformed other models in predicting the sentiment of tweets related to hospital services during the COVID-19 pandemic. This study’s findings may have significant implications for healthcare providers in enhancing their services’ quality and improving patients’ satisfaction during pandemics.
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