Name
Abstract | ||
|---|---|---|
Sentiment analysis predicts a one-dimensional quantity describing the positive or negative emotion of an author. Mood analysis extends the one-dimensional sentiment response to a multi-dimensional quantity, describing a diverse set of human emotions. In this paper, we extend sentiment and mood analysis temporally and model emotions as a function of time based on temporal streams of blog posts authored by a specific author. The model is useful for constructing predictive models and discovering scientific models of human emotions. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Mood,Computer science,Sentiment analysis,Scientific modelling,Artificial intelligence,Natural language processing,Multimedia,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seungyeon Kim | 1 | 92 | 5.61 |
| Joonseok Lee | 2 | 124 | 5.47 |
| Guy Lebanon | 3 | 936 | 80.79 |
| Haesun Park | 4 | 3546 | 232.42 |