Name
Abstract | ||
|---|---|---|
Individual mood is important for physical and emotional well-being, creativity and working memory. However, due to the lack of long-term real tracking daily data in individual level, most current works focus their efforts on population level and short-term small group. An ignored yet important task is to find the sentiment spreading mechanism in individual level from their daily behavior data. This paper studies this task by raising the following fundamental and summarization question, being not sufficiently answered by the literature so far:Given a social network, how the sentiment spread?The current individual-level network spreading models always assume one can infect others only when he/she has been infected. Considering the negative emotion spreading characters in individual level, we loose this assumption, and give an individual negative emotion spreading model. In this paper, we propose a Graph Coupled Hidden Markov Sentiment Model for modeling the propagation of infectious negative sentiment locally within a social network.Taking the MIT Social Evolution dataset as an example, the experimental results verify the efficacy of our techniques on real-world data. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Mood,Automatic summarization,Population,Social network,Social evolution,Computer science,Working memory,Artificial intelligence,Hidden Markov model,Creativity,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhanwei Du | 1 | 1 | 0.35 |
| Yongjian Yang | 2 | 39 | 14.05 |
| Chuang Ma | 3 | 16 | 7.00 |
| Yuan Bai | 4 | 2 | 1.38 |