Name
Abstract | ||
|---|---|---|
Optimism is linked to various personality factors as well as both psychological and physical health, but how does it relate to the way a person tweets? We analyze the online activity of a set of Twitter users in order to determine how well machine learning algorithms can detect a person's outlook on life by reading their tweets. A sample of tweets from each user is manually annotated in order to establish ground truth labels, and classifiers are trained to distinguish between optimistic and pessimistic users. Our results suggest that the words in people's tweets provide ample evidence to identify them as optimists, pessimists, or somewhere in between. Additionally, several applications of these trained models are explored. |
Year | Venue | DocType |
|---|---|---|
2016 | PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2 | Conference |
Volume | Citations | PageRank |
P16-2 | 2 | 0.40 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianzhi Ruan | 1 | 2 | 0.40 |
| Steven R. Wilson | 2 | 12 | 7.21 |
| Rada Mihalcea | 3 | 4 | 1.46 |