Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11280-013-0221-9 | World Wide Web |
Keywords | Field | DocType |
Web 2.0,Social emotion detection,Emotional dictionary,Topic modeling | Social emotions,Social media,Information retrieval,Computer science,Sentiment analysis,Microblogging,Emotion detection,Natural language processing,Artificial intelligence,Web 2.0,Topic model | Journal |
Volume | Issue | ISSN |
17 | 4 | 1386-145X |
Citations | PageRank | References |
54 | 1.44 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanghui Rao | 1 | 256 | 23.32 |
| Jingsheng Lei | 2 | 691 | 69.87 |
| Liu Wenyin | 3 | 2531 | 215.13 |
| Qing Li | 4 | 3222 | 433.87 |
| Mingliang Chen | 5 | 90 | 7.06 |