Building emotional dictionary for sentiment analysis of online news

被引：125

作者：

Rao, Yanghui ^{[1
]}

Lei, Jingsheng ^{[2
]}

Liu Wenyin ^{[2
]}

Li, Qing ^{[1
]}

Chen, Mingliang ^{[3
]}

机构：

[1] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China

[2] Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai, Peoples R China

[3] City Univ Hong Kong, Ctr Transport Trade & Financial Studies, Kowloon, Hong Kong, Peoples R China

来源：

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS | 2014年 / 17卷 / 04期

关键词：

Web; 2.0; Social emotion detection; Emotional dictionary; Topic modeling;

D O I：

10.1007/s11280-013-0221-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level emotional dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, a method based on topic modeling is proposed to construct a topic-level dictionary, where each topic is correlated with social emotions. Experiment on the real-world data sets has validated the effectiveness and reliability of the methods. Compared with other lexicons, the dictionary generated using our approach is language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles, identifying social emotions on certain entities and news events.

引用

页码：723 / 742

页数：20

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