AbstractAspect-based sentiment analysis (ABSA) is a powerful way of predicting the sentiment polarity of text in natural language processing. However, understanding human emotions and reasoning from text like a human continues to be a challenge. In this paper, we propose a model, named Attention-based Sentiment Reasoner (AS-Reasoner), to alleviate the problem of how to capture precise sentiment expressions in ABSA for reasoning. AS-Reasoner assigns importance degrees to different words in a sentence to capture key sentiment expressions towards a specific aspect, and transfers them into a sentiment sentence representation for reasoning in the next layer. To obtain appropriate importance degree values for different words in a sentence, two attention mechanisms we designed: intra attention and global attention. Specifically, intra attention captures the sentiment similarity between any two words in a sentence to compute weights and global attention computes weights by a global perspective. Experiments on all four English and four Chinese datasets show that the proposed model achieves state-of-the-art accuracy and macro-F1 results for aspect term level sentiment analysis and obtains the best accuracy for aspect category level sentiment analysis. The experimental results also indicate that AS-Reasoner is language-independent.