QQ客服 官方微博 反馈留言

	News sentiment analysis based on cross-domain sentiment word lists and content classifiers
	Yan, Lun ; Zhang, Yan
	2012
英文摘要	The main task of sentiment classification is to automatically judge sentiment polarity (positive or negative) of published sentiment data (e.g. news or reviews). Some researches have shown that supervised methods can achieve good performance for blogs or reviews. However, the polarity of a news report is hard to judge. Web news reports are different from other web documents. The sentiment features in news are less than the features in other Web documents. Besides, the same words in different domains have different polarity. So we propose a self-growth algorithm to generate a cross-domain sentiment word list, which is used in sentiment classification of Web news. This paper considers some previously undescribed features for automatically classifying Web news, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms, and also validates the self-growth algorithm for the cross-domain word list. ? Springer-Verlag 2012.; EI; 0
语种	英语
DOI标识	10.1007/978-3-642-35527-1_48
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/411874]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yan, Lun,Zhang, Yan. News sentiment analysis based on cross-domain sentiment word lists and content classifiers. 2012-01-01.

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。