Aspect-based Sentiment Classification (ASC) task is a challenge in Natural Language Processing (NLP) and is especially important for fields that require detailed analysis like finance. It aims to identify the sentiment polarity of specific aspects in sentences. In addition to tweets and posts directly related to finance, news from such as restaurants and e-commerce may also indirectly affect its stock prices. In previous approaches, attention-based neural network models were mostly adopted to implicitly connect aspects with opinion words for better aspect representations. However, due to the complexity of language and the presence of multiple aspects in a single sentence, these existing models often confuse connections. To tackle this problem, we propose a model named GAS-CL which encodes syntactical structure into aspect representations and refines it with a contrastive loss. Experiments on several datasets confirm that our approach can have better aspect representations and achieve a significant improvement.