LDA2Net: Digging Under the Surface of COVID-19 scientific literature Topics via a Network-Based Approach
This repository contains supplementary information for exploring the topics of the manuscript "LDA2Net: Digging Under the Surface of COVID-19 scientific literature Topics via a Network-Based Approach" (G. Minello, C.R.M.A. Santagiustina, M. Warglien), submitted to PLOS ONE and currently under review. A preliminary (Working Paper) version of the manuscript is publicly available at:
dx.doi.org/10.13140/RG.2.2.31634.99524
Supplementary information includes:
- The Beta parameters matrix, containing the distribution of words in each topic, which can be downloaded at the following link: https://drive.google.com/file/d/1awk3UTy7tyJXhKNSve8YQSWdfWQujC4O/view?usp=sharing
In addition, for each topic of the estimated LDA model (with 120 topics) a PDF file with the following information is provided:
- Human label and automatic n-gram label proposals.
- Summary measures.
- Network of top bigrams.
- Wordclouds of top words by node relevance measure.
- Wordclouds of top bigrams by edge relevance measure.
- Filtered topic network.