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Aspect and opinion term extraction is a critical step in Aspect-BasedSentiment Analysis (ABSA). Our study focuses on evaluating transfer learningusing pre-trained BERT (Devlin et al., 2018) to classify tokens from hotelreviews in bahasa Indonesia. The primary challenge is the language informalityof the review texts. By utilizing transfer learning from a multilingual model,we achieved up to 2% difference on token level F1-score compared to thestate-of-the-art Bi-LSTM model with fewer training epochs (3 vs. 200 epochs).The fine-tuned model clearly outperforms the Bi-LSTM model on the entity level.Furthermore, we propose a method to include CRF with auxiliary labels as anoutput layer for the BERT-based models. The CRF addition further improves theF1-score for both token and entity level.

Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels