LDAShiny: Interactive Topic Modeling and Bibliometric Analysis via Shiny
Provides a 'Shiny' graphical interface for the complete workflow of
Latent Dirichlet Allocation (LDA) topic modelling on bibliometric data from
Scopus and Web of Science. Steps include data import and deduplication, text
preprocessing (stopword removal, stemming, n-grams, sparse-term filtering),
statistical inference to select the optimal number of topics via coherence,
final model training, and topic trend analysis over time using linear
regression. All results can be exported as Excel files, RDS objects, and
publication-quality plots.
| Version: |
1.0.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
colourpicker, config (≥ 0.3.1), dplyr, DT, ggplot2, golem (≥
0.4.0), Matrix, openxlsx, quanteda, RColorBrewer, readxl, shiny (≥ 1.7.0), shinybusy, shinydashboard, shinyjs, shinyWidgets, slam, SnowballC, stopwords, textmineR, tibble, tidyr, tm, wordcloud, broom, parallel, stats, utils, grDevices |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0), withr |
| Published: |
2026-06-08 |
| DOI: |
10.32614/CRAN.package.LDAShiny |
| Author: |
Javier De La Hoz-M
 [aut, cre] |
| Maintainer: |
Javier De La Hoz-M <jdelahoz at unimagdalena.edu.co> |
| License: |
GPL-3 |
| URL: |
https://github.com/JavierDeLaHoz/LDAShiny |
| NeedsCompilation: |
no |
| Language: |
en-US |
| CRAN checks: |
LDAShiny results |
Documentation:
Downloads:
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