Aspect and Opinion Term Extraction for Aspect Based Sentiment Analysis of Hotel Reviews Using Transfer Learning

One of the tasks in aspect-based sentiment analysis is to extract aspect and opinion terms from review text. Our study focuses on evaluating transfer learning using BERT (Devlin et al., 2019) to classify tokens from hotel reviews in bahasa Indonesia. We show that the default BERT model failed to outperform a simple argmax method. However, changing the default BERT tokenizer to our custom one can improve the F1 scores on our labels of interest by at least 5%. For I-ASPECT and B-SENTIMENT, it can even increased the F1 scores by 11%. On entity-level evaluation, our tweak on the tokenizer can achieve F1 scores of 87% and 89% for ASPECT and SENTIMENT labels respectively. These scores are only 2% away from the best model by Fernando et al. (2019), but with much less training effort (8 vs 200 epochs).