Aspect Category Detection via Topic-Attention Network

TL;DR

This paper introduces a deep neural network with a topic-attention mechanism for aspect category detection in review sentences, improving accuracy over existing methods in restaurant domain datasets.

Contribution

The paper presents a novel attention-based neural network that uses multiple topic contexts to better identify aspect categories in review sentences.

Findings

Outperforms existing methods on SemEval restaurant datasets

Effective visualization of attention weights shows focus on relevant words

Model demonstrates robustness across multiple datasets

Abstract

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.