Representation learning for aspect category detection in online reviews

TLDR: This paper proposes a representation learning approach to automatically learn useful features for aspect category detection and achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.

Abstract: User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., "food" and "service" in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage handcrafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.