A Novel Hierarchical Attention-based Method for Aspect-level Sentiment Classification

Aspect-level sentiment classification is an essential issue in the sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. The recent methods have discovered the roles of some aspects in sentiment polarity classification and have developed various techniques to assess the sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspects. In order to address this issue, in the present work, we suggest a Hierarchical Attention-based Method (HAM) for the aspect-based polarity classification of the text. HAM works in a hierarchically manner. Firstly, it extracts an embedding vector for the aspects. Next, it employs these aspect vectors with information content to determine the sentiment of the text. The experimental findings on the SemEval2014 dataset show that HAM can improve the accuracy by up to 6.74% compared to the state-of-the-art methods in the aspect-based sentiment classification task.