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 successful 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 aspect information
into the neural model. More specifically, we incorporate aspect information
into the neural model by modeling word-aspect relationships. Our novel model,
\textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative
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 circular convolution
and circular correlation to model the similarity between aspect and words and
elegantly incorporates this within a differentiable neural attention framework.
Finally, our model is end-to-end differentiable and highly related to
convolution-correlation (holographic like) memories. Our proposed neural model
achieves state-of-the-art performance on benchmark datasets, outperforming
ATAE-LSTM by 4%−5% on average across multiple datasets.Comment: Accepted to AAAI201
