Sentiment Analysis Using LSTM Algorithm Regarding Grab Application Services in Indonesia
DOI:
https://doi.org/10.30871/jaic.v9i2.8696Keywords:
Sentiment Analysis, User Reviews, Long Short-Term Memory, Bi-Directional LSTM, Multi-Head AttentionAbstract
This study aims to analyze the sentiment of user reviews for the Grab Indonesia application using Long Short-Term Memory (LSTM) algorithms. Two variants of LSTM, namely Stacked LSTM and Bi-Directional LSTM, were compared to determine the most effective model in classifying user review sentiments. Both models were enhanced with Multi-Head Attention mechanisms to capture more complex contextual relationships in sequential data. The data used consists of 2,000 user reviews collected through scraping from the Google Play Store, with sentiment labels of positive and negative. Data preprocessing included labeling, case folding, stopword removal, tokenization, stemming, and the application of the SMOTE technique to address class imbalance. The results show that the Bi-Directional LSTM model achieved the highest validation accuracy of 87%, with an F1-score of 0.90 for the negative class and 0.82 for the positive class, while the Stacked LSTM recorded an accuracy of 84%, with an F1-score of 0.87 for the negative class and 0.78 for the positive class. Overall, the Bi-Directional LSTM demonstrated better performance in identifying both negative and positive sentiments, providing a good balance between precision and recall. This study proves that Bi-Directional LSTM with Multi-Head Attention can improve sentiment analysis performance on user reviews of digital applications, with potential applications in various other platforms.
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