Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics
Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the dem...
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MDPI AG
2025-01-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/1/7 |
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| author | Carlo Galli Maria Teresa Colangelo Marco Meleti Stefano Guizzardi Elena Calciolari |
| author_facet | Carlo Galli Maria Teresa Colangelo Marco Meleti Stefano Guizzardi Elena Calciolari |
| author_sort | Carlo Galli |
| collection | DOAJ |
| description | Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands of such rapidly expanding domains. In this study, we employed BERTopic, a transformer-based topic modeling framework, to map the thematic landscape of periodontics research published in MEDLINE from 2009 to 2024. We identified 31 broad topics encompassing four major thematic axes—patient management, periomedicine, oral microbiology, and implant-related surgery—thereby illuminating core areas and their semantic relationships. Compared with a conventional Latent Dirichlet Allocation (LDA) approach, BERTopic yielded more contextually nuanced clusters and facilitated the isolation of distinct, smaller research niches. Although some documents remained unlabeled, potentially reflecting either semantic ambiguity or niche topics below the clustering threshold, our results underscore the flexibility, interpretability, and scalability of neural topic modeling in this domain. Future refinements—such as domain-specific embedding models and optimized granularity levels—could further enhance the precision and utility of this method, ultimately guiding researchers, educators, and policymakers in navigating the evolving landscape of periodontics. |
| format | Article |
| id | doaj-art-a0c768329c8a46f9a00b49915114cf0e |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-a0c768329c8a46f9a00b49915114cf0e2025-01-24T13:22:32ZengMDPI AGBig Data and Cognitive Computing2504-22892025-01-0191710.3390/bdcc9010007Topic Analysis of the Literature Reveals the Research Structure: A Case Study in PeriodonticsCarlo Galli0Maria Teresa Colangelo1Marco Meleti2Stefano Guizzardi3Elena Calciolari4Histology and Embryology Laboratory, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43126 Parma, ItalyHistology and Embryology Laboratory, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43126 Parma, ItalyDepartment of Medicine and Surgery, Dental School, University of Parma, 43126 Parma, ItalyHistology and Embryology Laboratory, Department of Medicine and Surgery, University of Parma, Via Volturno 39, 43126 Parma, ItalyDepartment of Medicine and Surgery, Dental School, University of Parma, 43126 Parma, ItalyPeriodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands of such rapidly expanding domains. In this study, we employed BERTopic, a transformer-based topic modeling framework, to map the thematic landscape of periodontics research published in MEDLINE from 2009 to 2024. We identified 31 broad topics encompassing four major thematic axes—patient management, periomedicine, oral microbiology, and implant-related surgery—thereby illuminating core areas and their semantic relationships. Compared with a conventional Latent Dirichlet Allocation (LDA) approach, BERTopic yielded more contextually nuanced clusters and facilitated the isolation of distinct, smaller research niches. Although some documents remained unlabeled, potentially reflecting either semantic ambiguity or niche topics below the clustering threshold, our results underscore the flexibility, interpretability, and scalability of neural topic modeling in this domain. Future refinements—such as domain-specific embedding models and optimized granularity levels—could further enhance the precision and utility of this method, ultimately guiding researchers, educators, and policymakers in navigating the evolving landscape of periodontics.https://www.mdpi.com/2504-2289/9/1/7periodonticstrending topicsnatural language processingdeep learningartificial intelligence |
| spellingShingle | Carlo Galli Maria Teresa Colangelo Marco Meleti Stefano Guizzardi Elena Calciolari Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics Big Data and Cognitive Computing periodontics trending topics natural language processing deep learning artificial intelligence |
| title | Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics |
| title_full | Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics |
| title_fullStr | Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics |
| title_full_unstemmed | Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics |
| title_short | Topic Analysis of the Literature Reveals the Research Structure: A Case Study in Periodontics |
| title_sort | topic analysis of the literature reveals the research structure a case study in periodontics |
| topic | periodontics trending topics natural language processing deep learning artificial intelligence |
| url | https://www.mdpi.com/2504-2289/9/1/7 |
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