R's tidytext turns messy text into valuable insight

Check out "Text Mining with R: A tidy approach" to learn about how tidy data principles and the tidytext package can help you perform text mining in R. "Many of us who work in analytical fields are not trained in even simple interpretation of natural language," write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach. The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document's most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they're using text mining on job postings at Stack Overflow, some of the challenges and best practices they've experienced when mining text, and how their tidytext package for R aims to make text analysis both easy and informative. Text and other unstructured data is increasingly important for data analysts and data scientists in diverse fields from health care to tech to nonprofits. This data can help us make good decisions, but to capitalize on it, we must have the tools and the skills to get from unstructured text to insights.