A Novel Joint Model with Second-Order Features and Matching Attention for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of the specific aspect for a given sentence. Attention-based models are widely used in this task because they can extract semantic information between context words to make up for the deficiency of sequence models in semantic encoding. In order to enhance the extraction of high-quality semantic information, we propose a novel joint model with Second-Order Features and Matching Attention (SOMA) for aspect-based sentiment analysis. Firstly, we introduce the second-order statistics to extract vital information and interact with the first-order features to generate the interaction representation. Secondly, we adopt Euclidean distance to replace the traditional matrix transformation to capture the semantic similarity between aspect terms and context words. Finally, we form a joint representation to focus on the meaningful words in the sentence. We conduct extensive experiments and comparisons on SemEval 2014, SemEval 2016, and Twitter datasets. Experimental results demonstrate the effectiveness of our model.