Topic modeling and classification of scientific disciplines

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985955%3A_____%2F22%3A00566673" target="_blank" >RIV/67985955:_____/22:00566673 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5281/zenodo.6957149" target="_blank" >https://doi.org/10.5281/zenodo.6957149</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Topic modeling and classification of scientific disciplines

Popis výsledku v původním jazyce

This paper evaluates the possibility of classifying Ph.D. theses into disciplines by using a bottom-up empirical approach based on topic modeling. It examines a dataset of 334810 Ph.D. theses submitted at French universities between 2006 and 2020. In this comprehensive dataset, the variable “discipline” does not rely on any controlled vocabulary or disciplinary ontology. Consequently, there are 23057 unique labels for the variable of which 14538 appear only once. Such situation renders impossible any full-scale analysis of the data from the perspective of scientific disciplines. Our topic model is built atop of abstracts of 285311 of theses in French that include a title, keywords, and abstract. After applying the TopSBM algorithm, we obtained a topic model with 7 levels of hierarchy. The outcomes of our experiments with classification of theses into disciplines suggest that topics derived from purely textual data implicitly capture information about disciplines. This quality of topic modelling can be of great benefit when dealing with datasets where disciplinary information is unavailable or unreliable and where citation records are absent (as it remains the case especially in the Humanities).

Název v anglickém jazyce

Topic modeling and classification of scientific disciplines

Popis výsledku anglicky

This paper evaluates the possibility of classifying Ph.D. theses into disciplines by using a bottom-up empirical approach based on topic modeling. It examines a dataset of 334810 Ph.D. theses submitted at French universities between 2006 and 2020. In this comprehensive dataset, the variable “discipline” does not rely on any controlled vocabulary or disciplinary ontology. Consequently, there are 23057 unique labels for the variable of which 14538 appear only once. Such situation renders impossible any full-scale analysis of the data from the perspective of scientific disciplines. Our topic model is built atop of abstracts of 285311 of theses in French that include a title, keywords, and abstract. After applying the TopSBM algorithm, we obtained a topic model with 7 levels of hierarchy. The outcomes of our experiments with classification of theses into disciplines suggest that topics derived from purely textual data implicitly capture information about disciplines. This quality of topic modelling can be of great benefit when dealing with datasets where disciplinary information is unavailable or unreliable and where citation records are absent (as it remains the case especially in the Humanities).

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

50803 - Information science (social aspects)

Projekt

<a href="/cs/project/GJ20-01752Y" target="_blank" >GJ20-01752Y: Grantový a negrantový výzkum v České republice: scientometrická analýza a modelování témat</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů