Developing Text Mining Web Applications for Teaching and Learning Using Shiny Apps

Abstract

Text mining has emerged as a vital technique for extracting insights from large volumes of textual data. This research paper presents the development of an interactive Shiny apps called TextCrunch to facilitate the teaching and learning of text mining principles. The application is an innovative educational tool that provides users, including educators and learners, with an intuitive platform to explore text data analysis techniques. Developed within the Shiny framework, the web application offers a comprehensive yet user-friendly interface. Users can upload text data and produce real time outputs through word clouds, frequency plots, and topic modeling results. The word cloud visualization highlights prominent terms, while frequency plots offer insights into term occurrences. The integration of topic modeling assists users in identifying latent topics within the text, adding depth to the analysis. This paper elaborates on the technical aspects of the application's development, including text preprocessing and the creation of interactive visualizations. By offering an environment for hands-on exploration of text mining concepts, educators can illustrate intricate data analysis procedures effectively, while learners can actively engage with the content, reinforcing their understanding of text mining techniques. This research contributes to the field by highlighting the potential of technology-driven tools in enhancing the teaching and learning of data analysis principles.