Imagine you're reading a book and you notice one character is cheerful and upbeat, while the second character is pessimistic and never has anything positive to say. If you wanted to share your observation, you might write a paper which integrates quotations paraphrased examples from the text.
Text analysis could help strengthen your argument. Text analysis uses computer scripts to "read" a text and identify patterns. Text analysis provides a range of outputs, including numerical counts of particular words or phrases, identifying positive/negative language (i.e. sentiment analysis). With additional temporal and/or geographic data, we can even identify how language trends changed trends over time and space.
There are a ton of ready-made toolkits for text analysis. This guide shares some of these options, as well as tips on prepping your dataset for analysis. I've included a hands-on tutorial teaching the basics of Constellate, Scopus, Voyant, and SentimentVis using datasets about climate change and global warming.
Topics:
Tools for Modeling Frequencies, Significant Terms, and Topics
Finding, Cleaning, and Uploading Textual Data
