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.

翻译：感官分析是对文字中观点和情绪的计算研究。深层次学习是一个模型,目前正在不同应用领域产生最新的最新信息,包括情绪分析。许多研究人员正在采用混合方法,将不同的深层次学习模式结合起来,并显示可以改进模型性能。在情绪分析中,文本数据输入首先转换成数字表示法。获取文本代表法的标准方法是精细调整的嵌入方法。但是,这种方法并不注意句子中每个字的上下文。因此,根据句子中文字的背景和位置,使用变换器的双向编码表示法(BERT)模型来获取文字表示法。这项研究扩展了以前使用BERT表示法进行的混合深度学习,用于印度尼西亚情绪分析。我们的模拟表明,BERT代表法改善了所有混合结构的精度。基于BERT的混合结构也比其他混合结构的精度略。