Description
Book Synopsis: Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With 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 and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages
Details
Looking to extract valuable insights from unstructured and text-heavy data? Look no further than "Text Mining with R: A Tidy Approach." This comprehensive book, written by renowned authors Julia Silge and David Robinson, introduces you to the power of text-mining techniques using tidytext, a package that applies tidy principles to text analysis in R. By treating text as data frames, you'll be able to easily manipulate, summarize, and visualize characteristics of text, opening up new possibilities for analysis and visualization.
One of the key advantages of this book is the integration of natural language processing (NLP) into effective workflows. Learn how to apply the tidy text format to NLP tasks, allowing you to gain deeper insights from literature, news, and social media. With practical code examples and data explorations, you'll be guided through sentiment analysis, frequency measurements, and topic modeling. Uncover the emotional content of text, identify important terms, and discover relationships between words using the power of the ggraph and widyr packages.
Whether you're a data analyst, researcher, or simply someone interested in unlocking the potential of text data, this book is a must-have. Explore fascinating case studies that delve into Twitter archives, NASA metadata, and Usenet messages. Gain a deeper understanding of document collections and classify them into natural groups. With "Text Mining with R: A Tidy Approach," you'll discover how to transform unstructured text into valuable insights.
Don't miss out on this opportunity to revolutionize your text analysis workflows. Get your copy of "Text Mining with R: A Tidy Approach" today and start leveraging the power of tidytext and NLP. Click here to order now!
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