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LDAShiny

Interactive Topic Modeling and Bibliometric Analysis via Shiny

v1.0.0 · Jun 7, 2026 · GPL-3

Description

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.

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r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
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r-oldrel-windows-x86_64 OK
r-release-macos-arm64 OK
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r-release-windows-x86_64 OK

Check History

OK 5 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 8, 2026

Dependency Network

Dependencies Reverse dependencies colourpicker config dplyr DT ggplot2 golem (>= 0.4.0) Matrix openxlsx quanteda RColorBrewer readxl shiny shinybusy shinydashboard shinyjs +10 more dependencies LDAShiny

Version History

new 1.0.0 Jun 8, 2026