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Aspect and opinion terms extraction from review texts is one of the key tasksin aspect-based sentiment analysis. In order to extract aspect and opinionterms for Indonesian hotel reviews, we adapt double embeddings feature andattention mechanism that outperform the best system at SemEval 2015 and 2016.We conduct experiments using 4000 reviews to find the best configuration andshow the influences of double embeddings and attention mechanism toward modelperformance. Using 1000 reviews for evaluation, we achieved F1-measure of 0.914and 0.90 for aspect and opinion terms extraction in token and entity (term)level respectively.

Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews