Abstract
In this study, we examined 6,834 master's and PhD theses conducted on computer science and engineering between 1994 and 2013 in Turkey. Thesis data were compiled from the YÖK national thesis database web portal. We used text mining techniques to extract research concepts and their co-occurrence data from graduate thesis abstracts. Then, we applied social network analysis techniques on the concept co-occurrence networks to visually explore core research concepts, and connections and relationships among them. We showed that text mining and social network analysis techniques together were very effective for knowledge discovery in scientific documents in computer science and engineering domain.
| Original language | English |
|---|---|
| Title of host publication | CompSysTech '14 |
| Subtitle of host publication | 15th International Conference on Computer Systems and Technologies |
| Editors | Boris Rachev, Angel Smrikarov |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery |
| Pages | 187-193 |
| Number of pages | 7 |
| ISBN (Print) | 9781450327534 |
| DOIs | |
| Publication status | Published - 27 Jun 2014 |
| Externally published | Yes |
| Event | 15th International Conference on Computer Systems and Technologies - Ruse, Bulgaria Duration: 27 Jun 2014 → 28 Jun 2014 |
Conference
| Conference | 15th International Conference on Computer Systems and Technologies |
|---|---|
| Abbreviated title | CompSysTech'14 |
| Country/Territory | Bulgaria |
| City | Ruse |
| Period | 27/06/14 → 28/06/14 |
Keywords
- graduate theses
- computer science
- computer engineering
- text mining
- social network analysis