Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews
This work addresses aspect-based sentiment analysis for Indonesian hotel reviews, representing an incremental improvement over existing methods.
The paper tackled aspect and opinion terms extraction from Indonesian hotel reviews by adapting double embeddings and an attention mechanism, achieving F1-measures of 0.914 and 0.90 for aspect and opinion terms at token and entity levels, respectively.
Aspect and opinion terms extraction from review texts is one of the key tasks in aspect-based sentiment analysis. In order to extract aspect and opinion terms for Indonesian hotel reviews, we adapt double embeddings feature and attention mechanism that outperform the best system at SemEval 2015 and 2016. We conduct experiments using 4000 reviews to find the best configuration and show the influences of double embeddings and attention mechanism toward model performance. Using 1000 reviews for evaluation, we achieved F1-measure of 0.914 and 0.90 for aspect and opinion terms extraction in token and entity (term) level respectively.