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Aspect-based sentiment analysis (ABSA) tries to predict the polarity of agiven document with respect to a given aspect entity. While neural networkarchitectures have been successful in predicting the overall polarity ofsentences, aspect-specific sentiment analysis still remains as an open problem.In this paper, we propose a novel method for integrating aspect informationinto the neural model. More specifically, we incorporate aspect informationinto the neural model by modeling word-aspect relationships. Our novel model,\textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associativerelationships between sentence words and aspect which allows our model toadaptively focus on the correct words given an aspect term. This amelioratesthe flaws of other state-of-the-art models that utilize naive concatenations tomodel word-aspect similarity. Instead, our model adopts circular convolutionand circular correlation to model the similarity between aspect and words andelegantly incorporates this within a differentiable neural attention framework.Finally, our model is end-to-end differentiable and highly related toconvolution-correlation (holographic like) memories. Our proposed neural modelachieves state-of-the-art performance on benchmark datasets, outperformingATAE-LSTM by $4\%-5\%$ on average across multiple datasets.

Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis