| Title |
Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics
|
|---|---|
| Published in |
Journal of Big Data, September 2021
|
| DOI | 10.1186/s40537-021-00511-0 |
| Authors |
Edwin Camilleri, Shah Jahan Miah |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 3 | 14% |
| Student > Ph. D. Student | 3 | 14% |
| Student > Doctoral Student | 2 | 10% |
| Librarian | 2 | 10% |
| Student > Postgraduate | 2 | 10% |
| Other | 5 | 24% |
| Unknown | 4 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 29% |
| Unspecified | 3 | 14% |
| Business, Management and Accounting | 3 | 14% |
| Agricultural and Biological Sciences | 2 | 10% |
| Social Sciences | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 5 | 24% |