QQ客服 官方微博 反馈留言

	Reader Emotion Classification of News Headlines
	Jia, Yuxiang ; Chen, Zhengyan ; Yu, Shiwen
	2009
关键词	Emotion classification support vector machine (SVM) news headlines
英文摘要	Emotion classification of text is very important in applications like emotional text-to-speech (TTS) synthesis, human computer interaction, etc. Past studies on emotion classification focus on the writer's emotional state conveyed through the text. This research addresses the reader's emotions provoked by the text. The classification of documents into reader emotion categories has novel applications. One of them is to integrate reader emotion classification into a web search engine to allow users to retrieve documents that contain relevant contents and at the same time produce proper emotions. Another is for websites to organize contents according to reader emotion categories and provide users a convenient browse. In this paper, we explore sentence level emotion classification. Firstly, we extract news headlines and related reader emotion information from the web. Then we classify news headlines into reader emotion categories using support vector machine (SVM), and examine classification performance under different feature settings. Experiments show that certain feature combinations achieve good results.; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/NLPKE.2009.5313762
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/406379]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Jia, Yuxiang,Chen, Zhengyan,Yu, Shiwen. Reader Emotion Classification of News Headlines. 2009-01-01.

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。