Walkthrough 5: Text analysis with social media data

This chapter explores tidying, transforming, visualizing, and analyzing text data. Data scientists in education are surrounded by text-based data sources like word processing documents, emails, and survey responses. Data scientists in education can expand their opportunities to learn about the student experience by adding text mining and natural language processing to their toolkit. Using Twitter data, this chapter shows the reader practical tools for text analysis, including preparing text data, counting and visualizing words, and doing sentiment analysis. The chapter uses Tweets from #tidytuesday, an R learning community, to put these techniques in an education context. Data science tools in this chapter include transforming text into data frames, filtering datasets for keywords, running sentiment analysis and algorithms, and visualizing data.