  Sentiment analysis is the computational study of opinions and emotions
ex-pressed in text. Deep learning is a model that is currently producing
state-of-the-art in various application domains, including sentiment analysis.
Many researchers are using a hybrid approach that combines different deep
learning models and has been shown to improve model performance. In sentiment
analysis, input in text data is first converted into a numerical
representation. The standard method used to obtain a text representation is the
fine-tuned embedding method. However, this method does not pay attention to
each word's context in the sentence. Therefore, the Bidirectional Encoder
Representation from Transformer (BERT) model is used to obtain text
representations based on the context and position of words in sentences. This
research extends the previous hybrid deep learning using BERT representation
for Indonesian sentiment analysis. Our simulation shows that the BERT
representation improves the accuracies of all hybrid architectures. The
BERT-based LSTM-CNN also reaches slightly better accuracies than other
BERT-based hybrid architectures.
