BERTopic Modelling with P53 in Ovarian Cancer

Oveh, R.O.; Adewunmi, Mary; Aziken, G.O.

doi:10.1109/ited56637.2022.10051483

Cited by 2 publications

(1 citation statement)

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“…It supports dynamic topic modeling (for dynamic modeling in other approaches, see Alghamdi and Alfalqi, 2015 ) and class-based topic modeling and allows for multilingual analysis. This method was applied in medicine (Oveh et al, 2022 ), quality management (Sánchez-Franco et al, 2023 ), environmental, social, and governance research (Lee et al, 2023 ), and other areas. In mental health research, BERTopic was applied, for instance, by Cowan et al ( 2022 ) to analyse unstructured speech samples from individuals with serious mental illness, primarily schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts

Lezhnina

2023

Front. Res. Metr. Anal.

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The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research.

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