Downloading R
R is a programming language popular with statisticians. The language itself looks a bit like math and includes integration with SPSS and has many packages for data visualization and for working with text mining. One disadvantage is that R relies on your data being on your computer and also relies on your computer processing power, so it processes more slowly than some alternatives.
RStudio is a popular interface for R and includes the console for inputting commands, a window for viewing visualizations, and documentation for installed packages, as well as user prompts.
Resources on How to Use R
The first book introduces some basics for how to use R, including how to install additional packages. The other two books are specific to text mining. The second book offers many exercises where you can copy and paste the code to follow along.
R for Data Science by ISBN: 9781491910399Introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience.
Text Mining with R by ISBN: 9781491981658With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext, natural language processing, sentiment analysis, and other tidy tools in R can make text analysis easier and more effective. Includes case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages.
The SAGE Handbook of Social Media Research Methods by ISBN: 9781473916326Covers the formulation of research questions through to the interpretation of findings. Includes chapters on specific social media platforms such as Twitter, Sina Weibo and Instagram, specific tools such as R and GATE, as well as critical chapters.
Text Mining with Machine Learning by ISBN: 9780429469275Publication Date: 2019-10-31Provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms.
