Abstract
Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention. We propose an efficient method of automatically building the word-emotion mapping dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, three different pruning strategies are proposed to refine the dictionary. Experiment on the real-world data sets has validated the effectiveness and reliability of the method. Compared with other lexicons, the dictionary generated using our approach is more adaptive for personalized data set, language-independent, fine-grained, and volume-unlimited. The generated dictionary has a wide range of applications, including predicting the emotional distribution of news articles and tracking the change of social emotions on certain events over time.
| Original language | English |
|---|---|
| Pages (from-to) | 28-39 |
| Number of pages | 12 |
| Journal | CEUR Workshop Proceedings |
| Volume | 917 |
| Publication status | Published - 1 Dec 2012 |
| Externally published | Yes |
| Event | 1st International Workshop on Sentiment Discovery from Affective Data 2012, SDAD 2012 - In Conjunction with ECML-PKDD 2012 - Bristol, United Kingdom Duration: 28 Sept 2012 → 28 Sept 2012 |
Keywords
- Emotion dictionary
- Maximum likelihood estimation
- Social emotion detection
ASJC Scopus subject areas
- General Computer Science