Dual-Attention Model for Aspect-Level Sentiment Classification
This work addresses sentiment analysis for specific aspects in text, but it is incremental as it builds on existing attention-based methods by adding syntactic information.
The paper tackles aspect-level sentiment classification by proposing a dual-attention model that incorporates dependency labels, achieving good performance on three datasets including SemEval 2014 and Twitter data.
I propose a novel dual-attention model(DAM) for aspect-level sentiment classification. Many methods have been proposed, such as support vector machines for artificial design features, long short-term memory networks based on attention mechanisms, and graph neural networks based on dependency parsing. While these methods all have decent performance, I think they all miss one important piece of syntactic information: dependency labels. Based on this idea, this paper proposes a model using dependency labels for the attention mechanism to do this task. We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset. Experimental results show that the dual attention model has good performance on all three datasets.