Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya.
Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natural language processing and text mining domains. Topic modelling with Latent Dirichlet Allocation is one such statistical tool that has been successfully applied to synthesize collections of legal, bi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0243208 |
| _version_ | 1819023682853404672 |
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| author | Leacky Muchene Wende Safari |
| author_facet | Leacky Muchene Wende Safari |
| author_sort | Leacky Muchene |
| collection | DOAJ |
| description | Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natural language processing and text mining domains. Topic modelling with Latent Dirichlet Allocation is one such statistical tool that has been successfully applied to synthesize collections of legal, biomedical documents and journalistic topics. We applied a novel two-stage topic modelling approach and illustrated the methodology with data from a collection of published abstracts from the University of Nairobi, Kenya. In the first stage, topic modelling with Latent Dirichlet Allocation was applied to derive the per-document topic probabilities. To more succinctly present the topics, in the second stage, hierarchical clustering with Hellinger distance was applied to derive the final clusters of topics. The analysis showed that dominant research themes in the university include: HIV and malaria research, research on agricultural and veterinary services as well as cross-cutting themes in humanities and social sciences. Further, the use of hierarchical clustering in the second stage reduces the discovered latent topics to clusters of homogeneous topics. |
| first_indexed | 2024-12-21T04:42:47Z |
| format | Article |
| id | doaj.art-868fb483a9074caabcf8e4ee5d294f8d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T04:42:47Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-868fb483a9074caabcf8e4ee5d294f8d2022-12-21T19:15:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01161e024320810.1371/journal.pone.0243208Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya.Leacky MucheneWende SafariUnsupervised statistical analysis of unstructured data has gained wide acceptance especially in natural language processing and text mining domains. Topic modelling with Latent Dirichlet Allocation is one such statistical tool that has been successfully applied to synthesize collections of legal, biomedical documents and journalistic topics. We applied a novel two-stage topic modelling approach and illustrated the methodology with data from a collection of published abstracts from the University of Nairobi, Kenya. In the first stage, topic modelling with Latent Dirichlet Allocation was applied to derive the per-document topic probabilities. To more succinctly present the topics, in the second stage, hierarchical clustering with Hellinger distance was applied to derive the final clusters of topics. The analysis showed that dominant research themes in the university include: HIV and malaria research, research on agricultural and veterinary services as well as cross-cutting themes in humanities and social sciences. Further, the use of hierarchical clustering in the second stage reduces the discovered latent topics to clusters of homogeneous topics.https://doi.org/10.1371/journal.pone.0243208 |
| spellingShingle | Leacky Muchene Wende Safari Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. PLoS ONE |
| title | Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. |
| title_full | Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. |
| title_fullStr | Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. |
| title_full_unstemmed | Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. |
| title_short | Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya. |
| title_sort | two stage topic modelling of scientific publications a case study of university of nairobi kenya |
| url | https://doi.org/10.1371/journal.pone.0243208 |
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