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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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CONTENTS
Preface = vii
1. The Tidy Text Format = 1
 Contrasting Tidy Text with Other Data Structures = 2
 The unnest_tokens Function = 2
 Tidying the Works of Jane Austen = 4
 The gutenbergr Package = 7
 Word Frequencies = 8
 Summary = 12
2. Sentiment Analysis with Tidy Data = 13
 The sentiments Dataset = 14
 Sentiment Analysis with Inner Join = 16
 Comparing the Three Sentiment Dictionaries = 19
 Most Common Positive and Negative Words = 22
 Wordclouds = 25
 Looking at Units Beyond Just Words = 27
 Summary = 29
3. Analyzing Word and Document Frequency : tf-idf = 31
 Term Frequency in Jane Austen''''s Novels = 32
 Zipf''''s Law = 34
 The bind_tf_idf Function = 37
 A Corpus of Physics Texts = 40
 Summary = 44
4. Relationships Between Words : N-grams and Correlations = 45
 Tokenizing by N-gram = 45
  Counting and Filtering N-grams = 46
  Analyzing Bigrams = 48
  Using Bigrams to Provide Context in Sentiment Analysis = 51
  Visualizing a Network of Bigrams with ggraph = 54
  Visualizing Bigrams in Other Texts = 59
 Counting and Correlating Pairs of Words with the widyr Package = 61
  Counting and Correlating Among Sections = 62
  Examining Pairwise Correlation = 63
 Summary = 67
5. Converting to and from Nontidy Formats = 69
 Tidying a Document-Term Matrix = 70
  Tidying Document Term Matrix Objects = 71
  Tidying dfm Objects = 74
 Casting Tidy Text Data into a Matrix = 77
 Tidying Corpus Objects with Metadata = 79
  Example : Mining Financial Articles = 81
 Summary = 87
6. Topic Modeling = 89
 Latent Dirichlet Allocation = 90
  Word-Topic Probabilities = 91
  Document-Topic Probabilities = 95
 Example : The Great Library Heist = 96
  LDA on Chapters = 97
  Per-Document Classification = 100
  By-Word Assignments : augment = 103
 Alternative LDA Implementations = 107
 Summary = 108
7. Case Study : Comparing Twitter Archives = 109
 Getting the Data and Distribution of Tweets = 109
 Word Frequencies = 110
 Comparing Word Usage = 114
 Changes in Word Use = 116
 Favorites and Retweets = 120
 Summary = 124
8. Case Study : Mining NASA Metadata = 125
 How Data Is Organized at NASA = 126
  Wrangling and Tidying the Data = 126
  Some Initial Simple Exploration = 129
 Word Co-ocurrences and Correlations = 130
  Networks of Description and Title Words = 131
  Networks of Keywords = 134
 Calculating tf-idf for the Description Fields = 137
  What Is tf-idf for the Description Field Words? = 137
  Connecting Description Fields to Keywords = 138
 Topic Modeling = 140
  Casting to a Document-Term Matrix = 140
  Ready for Topic Modeling = 141
  Interpreting the Topic Model = 142
  Connecting Topic Modeling with Keywords = 149
 Summary = 152
9. Case Study : Analyzing Usenet Text = 153
 Preprocessing = 153
  Preprocessing Text = 155
 Words in Newsgroups = 156
  Finding tf-idf Within Newsgroups = 157
  Topic Modeling = 160
 Sentiment Analysis = 163
  Sentiment Analysis by Word = 164
  Sentiment Analysis by Message = 167
  N-gram Analysis = 169
 Summary = 171
Bibliography = 173
Index = 175