Locate and Combine: A Two-Stage Framework for Aspect-Category Sentiment Analysis

Yang Wu, Zhenyu Zhang, Yanyan Zhao, Bing Qin

Abstract: Aspect category sentiment classification aims at predicting the sentiment polarity of the given aspect category. Since the aspect category may not occur in the sentence, it is hard for the model to directly find the appropriate sentiment words for the aspect category and disregard unrelated ones. To address it, previous works have explored leveraging implicitly the information of the aspect term in the sentence and demonstrated the effectiveness of such information. Inspired by this conclusion, we propose a two-stage strategy named Locate-Combine(LC) to utilize the aspect term in a more straightforward way, which first locates the aspect term and then takes it as the bridge to find the related sentiment words. Specifically, in the “Locate” stage, we locate the aspect term corresponding to the given aspect category in the sentence, which can crystallize the target and further enable our model to focus on the target-related words. In the “Combine” stage, we first apply the graph convolutional network (GCN) over the dependency tree of the sentence to combine the information of the aspect term and related sentiment words and then take the output representation corresponding to the located aspect term to predict the sentiment polarity. The experimental results on the public datasets show that the proposed two-stage strategy is effective, which achieves state-of-the-art performance. Furthermore, our model can output explainable intermediate results for model analysis. (Code can be found at https://github.com/SCIR-MSA-Team/LC-ACSA )