期刊
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
卷 17, 期 4, 页码 723-742出版社
SPRINGER
DOI: 10.1007/s11280-013-0221-9
关键词
Web 2.0; Social emotion detection; Emotional dictionary; Topic modeling
资金
- Strategic Research Grant from City University of Hong Kong [7002912]
- Natural Science Foundation of China [61272437, 61073189]
- Innovation Program of Shanghai Municipal Education Commission [13ZZ131]
- Foundation Key Project of Shanghai Science and Technology Committee [12JC1404500]
- Shanghai Science and Technology Committee [12510500700]
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.
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