Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics
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Camilleri, Edwin 
ORCID: 0000-0003-0163-944X and Miah, Shah Jahan ORCID: 0000-0002-3783-8769
(2021)
Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics.
Journal of Big Data, 8.
ISSN 2196-1115
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| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/45198 |
| DOI | 10.1186/s40537-021-00511-0 |
| Official URL | https://journalofbigdata.springeropen.com/articles... |
| Subjects | Current > FOR (2020) Classification > 4602 Artificial intelligence Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
| Keywords | natural language processing; topic modelling; network theory; text mining |
| Citations in Scopus | 0 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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