Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document with respect to a given aspect entity. While neural network architectures have been suc- cessful in predicting the overall polarity of sentences, aspect- specific sentiment analysis still remains as an open problem. In this paper, we propose a novel method for integrating as- pect information into the neural model. More specifically, we incorporate aspect information into the neural model by mod- eling word-aspect relationships. Our novel model, Aspect Fu- sion LSTM (AF-LSTM) learns to attend based on associa- tive relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term. This ameliorates the flaws of other state-of-the-art models that utilize naive concatenations to model word-aspect similarity. Instead, our model adopts cir- cular convolution and circular correlation to model the simi- larity between aspect and words and elegantly incorporates this within a differentiable neural attention framework. Fi- nally, our model is end-to-end differentiable and highly re- lated to convolution-correlation (holographic like) memories. Our proposed neural model achieves state-of-the-art perfor- mance on benchmark datasets, outperforming ATAE-LSTM by 4% − 5% on average across multiple datasets.