MatchMaker: Aspect-Based Sentiment Classification via Mutual Information

Aspect-based sentiment classification (ABSC) aims to determine the sentiment polarity toward a specific aspect. In order to finish this task, it is difficult to match a specific aspect with its opinion words since there are usually multiple aspects with different opinion words in a sentence. Many efforts have been made to address this problem, such as graph neural networks and attention mechanism, however come at the cost of the introduced extraneous noise, leading to mismatches of the aspect with its opinion words. In this paper, we propose a Mutual Information-based ABSC model, called MatchMaker, which introduces Mutual Information estimation to strengthen the correlations between a specific aspect and its opinion words without introducing any extraneous noise, thus significantly improving the accuracy when determining the sentiment polarity toward a specific aspect. Experimental results show that our method with Mutual Information is effective. For example, MatchMaker obtains a significant improvement of accuracy over ASGCN model by 3.1% on the Rest14.