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

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• 作者演示了如何将文本作为数据框架来处理，使你能够操作、总结和可视化文本的特征。你还将学习如何将自然语言处理（NLP）纳入有效的工作流程。实用的代码示例和数据探索将帮助你从文献、新闻和社交媒体中产生真正的洞察力。
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• 学习如何将整齐的文本格式应用于NLP
• 使用情感分析来挖掘文本的情感内容
• 用频率测量法识别文件中最重要的术语
• 用ggraph和widyr软件包探索词语之间的关系和联系
• 在R的整洁和非整洁文本格式之间来回转换
• 使用主题建模将文件集分类为自然组
• 检查比较Twitter档案的案例研究，挖掘NASA元数据，并分析成千上万的Usenet信息