Topic modelling through the bibliometrics lens and its technique 2025

Topic modelling (TM) is a significant natural language processing (NLP) task and is becoming more popular, especially, in the context of literature synthesis and analysis. Despite the growing volume of studies on the use of and versatility of TM, the knowledge of TM development, especially from the perspective of bibliometrics analysis is limited. To this end, this study evaluated TM research using two techniques namely, bibliometrics analysis and TM itself to provide the current status and the pathway for future studies in the TM field. For this purpose, this study used 16,941 documents collected from Scopus database from 2004 to 2023. Results indicate that the publications on TM have increased over the years, however, the citation impact has declined. Furthermore, the scientific production on TM is concentrated in two countries namely, China and the USA. Our findings showed there are several applications of TM that are understudied, for example, TM for image segmentation and classification. This paper highlighted the future research directions, most importantly, calls for increased multidisciplinary research approaches to fully deploy TM algorithms optimally and thus, increase usage in non-computer science subject areas.

Autores	Kohler, AF; Digiampietri, LA
Título	Classification of authors, institutions, and countries, using productivity, centrality, and impact metrics: The field of tourism studies in Brazil (journals), 1990-2018
Afiliaciones	Universidade de Sao Paulo
Año	2021
DOI	10.7784/rbtur.v15i3.2035
Tipo de acceso abierto	Green Published, gold
Referencia	WOS:000891850400004
Artículo obtenido de:	WOS

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Topic modelling through the bibliometrics lens and its technique

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