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	Representation learning for aspect category detection in online reviews
	Zhou, Xinjie ; Wan, Xiaojun ; Xiao, Jianguo
	2015
英文摘要	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. Copyright ? 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; EI; 417-423; 1
语种	英语
出处	29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436798]
专题	计算机科学技术研究所 信息科学技术学院
推荐引用方式 GB/T 7714	Zhou, Xinjie,Wan, Xiaojun,Xiao, Jianguo. Representation learning for aspect category detection in online reviews. 2015-01-01.

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