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Sentiment analysis is the computational study of opinions and emotionsex-pressed in text. Deep learning is a model that is currently producingstate-of-the-art in various application domains, including sentiment analysis.Many researchers are using a hybrid approach that combines different deeplearning models and has been shown to improve model performance. In sentimentanalysis, input in text data is first converted into a numericalrepresentation. The standard method used to obtain a text representation is thefine-tuned embedding method. However, this method does not pay attention toeach word's context in the sentence. Therefore, the Bidirectional EncoderRepresentation from Transformer (BERT) model is used to obtain textrepresentations based on the context and position of words in sentences. Thisresearch extends the previous hybrid deep learning using BERT representationfor Indonesian sentiment analysis. Our simulation shows that the BERTrepresentation improves the accuracies of all hybrid architectures. TheBERT-based LSTM-CNN also reaches slightly better accuracies than otherBERT-based hybrid architectures.

BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis