QQ客服 官方微博 反馈留言

	An Empirical Study of Massively Parallel Bayesian Networks Learning for Sentiment Extraction from Unstructured Text
	Chen, Wei ; Zong, Lang ; Huang, Weijing ; Ou, Gaoyan ; Wang, Yue ; Yang, Dongqing
	2011
关键词	Sentiment Analysis Bayesian Networks MapReduce Cloud Computing Opinion Mining
英文摘要	Extracting sentiments from unstructured text has emerged as an important problem in many disciplines, for example, to mine online opinions from the Internet. Many algorithms have been applied to solve this problem. Most of them fail to handle the large scale web data. In this paper, we present a parallel algorithm for BN(Bayesian Networks) structure leaning from large-scale dateset by using a Map Reduce cluster. Then, we apply this parallel BN learning algorithm to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. The benefits of using Map Reduce for BN structure learning are discussed. The performance of using BN to extract sentiments is demonstrated by applying it to real web blog data. Experimental results on the web data set show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several usually used methods.; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1007/978-3-642-20291-9_47
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/406125]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Chen, Wei,Zong, Lang,Huang, Weijing,et al. An Empirical Study of Massively Parallel Bayesian Networks Learning for Sentiment Extraction from Unstructured Text. 2011-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。