Year: 2023
Type: article
Abstract: Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.
Source: IEEE Access
Authors: Christian-Daniel Curiac, Mihai V. Micea
Institution: Politehnica University of Timisoara
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,950
OpenAlex ID: W4322707152