Aspect-level sentiment classification aims to identify the sentiment polarity of a review expressed toward a target. In recent years, neural network-based methods have achieved success in aspect-level sentiment classification, and these methods fall into two types: the first takes the target information into account for context modelling, and the second models the context without considering the target information. It is concluded that the former is better than the latter. However, most of the target-related models just focus on the impact of the target on context modelling, while ignoring the role of context in target modelling. In this study, we introduce an interactive neural network model named LT-T-TR, which divided a review into three parts: the left context with target phrase, the target phrase, and the right context with target phrase. And the interaction between the left/right context and the target phrase is utilized by an attention mechanism to learn the representations of the left/right context and the target phrase separately. As a result, the most important words in the left/right context or in the target phrase are captured, and the results on laptop and restaurant datasets demonstrate that our model outperforms the state-of-the-art methods.