@article{Kavvadias_JBI_2020,
author={Spyridon Kavvadias and George Drosatos and Eleni Kaldoudi},
booktitle={Journal of Biomedical Informatics},
title={Supporting Topic Modeling and Trends Analysis in Biomedical Literature},
year={2020},
volume={110},
pages={103574},
keywords={Topic Modeling, Semantic Analysis, Trend Analysis, Web Application, Visualization},
doi={10.1016/j.jbi.2020.103574},
url={https://doi.org/10.1016/j.jbi.2020.103574},
issn={1532-0464},
abstract={Topic modeling refers to a suite of probabilistic algorithms for extracting popular topics from a collection of documents. A common approach involves the use of the Latent Dirichlet Allocation (LDA) algorithm, and, although free implementations are available, their deployment in general requires a certain degree of programming expertise. This paper presents a user-friendly web-based application, specifically designed for the biomedical professional, that supports the entire process of topic modeling and comparative trends analysis of scientific literature. The application was evaluated for its efficacy and usability by intended users with no programming expertise (15 biomedical professionals). Results of evaluation showed a positive acceptance of system functionalities and an overall usability score of 76/100 in the System Usability Score (SUS) scale. This suggests that literature topic modeling can become more popular amongst biomedical professionals via the use of a userfriendly application that fully supports the entire workflow, thus opening new perspectives for literature review and scientific research.}
}