Improving Neutral Sentiment Classification in Indonesian E-Wallet Reviews Using Word2Vec and Easy Data Augmentation (EDA)
Keywords:
Sentiment analysis, Word2Vec, easy data augmentation, LSTM, BiLSTMAbstract
The rapid expansion of digital payments has produced massive volumes of user-generated reviews, making manual analysis impractical. This study focuses on the challenge of neutral sentiment classification in Indonesian e-wallet reviews, where neutral comments often contain ambiguous language and are underrepresented relative to positive and negative classes. A total of 26,537 preprocessed DANA application reviews were used to evaluate whether Word2Vec embeddings and Easy Data Augmentation (EDA) can improve neutral sentiment detection when combined with Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures. Experiments comparing eight model configurations showed that the combination of Word2Vec, EDA, and LSTM achieved the best performance, with 0.861 accuracy, 0.841 macro-F1, and 0.749 F1-score for the neutral class. These findings demonstrate that semantic representations and controlled lexical variation can jointly enhance minority-class recognition in short informal Indonesian text and highlight the importance of aligning embedding strategies with sequence architectures.References
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Copyright (c) 2026 Muhammad Fattah Edric Camilo, Fatma Indriani, Mohammad Reza Faisal, Dwi Kartini, Dodon Turianto Nugrahadi
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