TextAnalysisR provides a supporting workflow for text mining analysis. The web app incorporates quanteda (text preprocessing), stm (structural topic modeling), ggraph, and widyr (network analysis). tidytext is implemented to tidy non-tidy format objects. The R Shiny web app is available at TextAnalysisR::TextAnalysisR.app() or https://textanalysisr.org. Functions are provided for completing word-topic probabilities, document-topic probabilities, estimated effects of covariates on topic prevalence, and network analysis, similar to tasks available in the web app.

Installation:

The development version from GitHub with:

install.packages("devtools")
devtools::install_github("mshin77/TextAnalysisR")

Run the Shiny App:

Access the web app at https://www.textanalysisr.org.

Launch and browser the TextAnalysisR.app on the local computer:

library(TextAnalysisR)
TextAnalysisR.app()

Citation:

• Shin, M. (2024). TextAnalysisR: Text mining workflow tools (R package version 0.0.2) [Computer software]. https://mshin77.github.io/TextAnalysisR

• Shin, M. (2024). TextAnalysisR: Text mining workflow tools [Web application]. https://www.textanalysisr.org

References:

• Shin, M., Park, H., & Kang, E. (2024). Development of education policies and practices for students with learning disabilities in South Korea using Delphi surveys and topic modeling. Learning Disability Quarterly. GitHub
• Shin, M., Ok, M. W., Choo, S., Hossain, G., Bryant, D. P., & Kang, E. (2023). A content analysis of research on technology use for teaching mathematics to students with disabilities: word networks and topic modeling. International Journal of STEM Education, 10(1), 1-23. GitHub