Name
Title | ||
|---|---|---|
Tweeting Your Mental Health: Exploration Of Different Classifiers And Features With Emotional Signals In Identifying Mental Health Conditions |
Abstract | ||
|---|---|---|
Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions as features and examined five popular machine learning classifiers in the task of identifying users with self-reported mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features and combined features showed promising improvements to the performance on this task. |
Year | Venue | Field |
|---|---|---|
2018 | PROCEEDINGS OF THE 51ST ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS) | Social media,Computer science,Support vector machine,Knowledge management,Cognitive psychology,Mental health,Random forest |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuetong Chen | 1 | 2 | 0.74 |
| Martin D. Sykora | 2 | 9 | 3.93 |
| Thomas W. Jackson | 3 | 217 | 17.90 |
| Suzanne Elayan | 4 | 3 | 1.44 |
| Fehmidah Munir | 5 | 0 | 0.34 |