Detecting Depression in Social Media : An Emotional Analysis Approach
| dc.contributor.author | Kim, Seunghyun | |
| dc.contributor.chair | Cardie, Claire T. | |
| dc.contributor.committeeMember | Bunea, Florentina | |
| dc.date.accessioned | 2019-10-15T15:29:05Z | |
| dc.date.available | 2019-10-15T15:29:05Z | |
| dc.date.issued | 2019-05-30 | |
| dc.description.abstract | Depression has been an ongoing mental health issue that has been affecting a wide range of humanity, particularly the young adults. To address and observe the more general public in a natural habitat, social media is examined for constructing a system to accurately detect depression. Despite the assiduous effort to construct a novel mechanism to detect depression from social media, behavioral approaches had underlying problems for users with a short activity span. To address this problem, emotion analysis was used as a tool to extract the emotion(s) of a user’s post to identify those with depression. Via machine learning techniques to construct an emotion classifier which in turn creates emotion embeddings for a binary classifier, this study proposes a pipeline structure to identify reddit posts from the depression subreddit. The model yielded promising results, introducing emotional analysis as a novel methodology in assessing mental health within social media. | |
| dc.identifier.doi | https://doi.org/10.7298/n9jz-1378 | |
| dc.identifier.other | Kim_cornell_0058O_10518 | |
| dc.identifier.other | http://dissertations.umi.com/cornell:10518 | |
| dc.identifier.other | bibid: 11050269 | |
| dc.identifier.uri | https://hdl.handle.net/1813/67287 | |
| dc.language.iso | en_US | |
| dc.subject | depression | |
| dc.subject | Computer science | |
| dc.subject | Social Media | |
| dc.subject | Emotional Analysis | |
| dc.title | Detecting Depression in Social Media : An Emotional Analysis Approach | |
| dc.type | dissertation or thesis | |
| dcterms.license | https://hdl.handle.net/1813/59810 | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Master of Science | |
| thesis.degree.name | M.S., Computer Science |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Kim_cornell_0058O_10518.pdf
- Size:
- 362.12 KB
- Format:
- Adobe Portable Document Format