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Mendeley readers
| 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 14% |
| Librarian | 2 | 9% |
| Student > Doctoral Student | 2 | 9% |
| Student > Postgraduate | 2 | 9% |
| Student > Master | 2 | 9% |
| Other | 4 | 18% |
| Unknown | 7 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 27% |
| Business, Management and Accounting | 3 | 14% |
| Agricultural and Biological Sciences | 2 | 9% |
| Unspecified | 1 | 5% |
| Social Sciences | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 8 | 36% |