tidytext: Text Mining and Analysis Using Tidy Data Principles in R

By Julia Silge, David Robinson. Journal of Open Source Software, 2016

837 citations recorded by the source corpus.

Tidy data sets allow manipulation with a standard set of “tidy” tools, including popular packages such as dplyr (Wickham, Francois, and RStudio 2015), ggplot2 (Wickham, Chang, and RStudio 2016), and broom (Robinson et al. 2015). These tools do not yet, however, have the infrastructure to work fluently with text data and natural language processing tools. In developing this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/1945711ed147087a65cf4ab163b8e21c38705273

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peter Brusilovsky
2. Chun-Hua Tsai

In Information Systems

1. Peter Brusilovsky
2. Chun-Hua Tsai
3. Vijayan Sugumaran

Citations in the corpus

• Designing Explanation Interfaces for Transparency and Beyond (2019, methodology): “The bar chart is a common approach in analyzing the text mining outcome [15] using a histogram of terms and term frequency.”