  The e-commerce has started a new trend in natural language processing through
sentiment analysis of user-generated reviews. Different consumers have
different concerns about various aspects of a specific product or service.
Aspect category detection, as a subtask of aspect-based sentiment analysis,
tackles the problem of categorizing a given review sentence into a set of
pre-defined aspect categories. In recent years, deep learning approaches have
brought revolutionary advances in multiple branches of natural language
processing including sentiment analysis. In this paper, we propose a deep
neural network method based on attention mechanism to identify different aspect
categories of a given review sentence. Our model utilizes several attentions
with different topic contexts, enabling it to attend to different parts of a
review sentence based on different topics. Experimental results on two datasets
in the restaurant domain released by SemEval workshop demonstrates that our
approach outperforms existing methods on both datasets. Visualization of the
topic attention weights shows the effectiveness of our model in identifying
words related to different topics.

Computation and Language