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	Aspect-specific polarity-aware summarization of online reviews
	Ou, Gaoyan ; Chen, Wei ; Liu, Peng ; Wang, Tengjiao ; Yang, Dongqing ; Lei, Kai ; Liu, Yueqin
	2013
英文摘要	With the popularity of various social media platforms, the number of online reviews towards different products and services grows dramatically. Discovering sentiments from online reviews becomes an important and challenging task in sentiment analysis. Current methods either extract aspects without separating aspects and sentiments, or extract aspects and sentiments without separating sentiments according to their polarities. In this paper, we propose two novel probabilistic generative models (APSM and ME-APSM) to extract aspects and aspect-specific polarity-aware sentiments from online reviews. We applied our models to two data sets with three different experiments. Experimental results show that APSM and ME-APSM models can extract aspects and polarity-aware sentiments well. For the sentiment classification task, our models outperform other generative models and come close to supervised classification methods. ? 2013 Springer-Verlag Berlin Heidelberg.; EI; 0
语种	英语
DOI标识	10.1007/978-3-642-38562-9-30
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294568]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Ou, Gaoyan,Chen, Wei,Liu, Peng,et al. Aspect-specific polarity-aware summarization of online reviews. 2013-01-01.

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