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Text mining with R : a tidy approach /
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 d...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2017.
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Copyright; Table of Contents; Preface; Outline; Topics This Book Does Not Cover; About This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgements; Chapter 1. The Tidy Text Format; Contrasting Tidy Text with Other Data Structures; The unnest_tokens Function; Tidying the Works of Jane Austen; The gutenbergr Package; Word Frequencies; Summary; Chapter 2. Sentiment Analysis with Tidy Data; The sentiments Dataset; Sentiment Analysis with Inner Join; Comparing the Three Sentiment Dictionaries; Most Common Positive and Negative Words.
- WordcloudsLooking at Units Beyond Just Words; Summary; Chapter 3. Analyzing Word and Document Frequency: tf-idf; Term Frequency in Jane Austen's Novels; Zipf's Law; The bind_tf_idf Function; A Corpus of Physics Texts; Summary; Chapter 4. Relationships Between Words: N-grams and Correlations; Tokenizing by N-gram; Counting and Filtering N-grams; Analyzing Bigrams; Using Bigrams to Provide Context in Sentiment Analysis; Visualizing a Network of Bigrams with ggraph; Visualizing Bigrams in Other Texts; Counting and Correlating Pairs of Words with the widyr Package.
- Counting and Correlating Among SectionsExamining Pairwise Correlation; Summary; Chapter 5. Converting to and from Nontidy Formats; Tidying a Document-Term Matrix; Tidying DocumentTermMatrix Objects; Tidying dfm Objects; Casting Tidy Text Data into a Matrix; Tidying Corpus Objects with Metadata; Example: Mining Financial Articles; Summary; Chapter 6. Topic Modeling; Latent Dirichlet Allocation; Word-Topic Probabilities; Document-Topic Probabilities; Example: The Great Library Heist; LDA on Chapters; Per-Document Classification; By-Word Assignments: augment; Alternative LDA Implementations.
- Casting to a Document-Term MatrixReady for Topic Modeling; Interpreting the Topic Model; Connecting Topic Modeling with Keywords; Summary; Chapter 9. Case Study: Analyzing Usenet Text; Preprocessing; Preprocessing Text; Words in Newsgroups; Finding tf-idf Within Newsgroups; Topic Modeling; Sentiment Analysis; Sentiment Analysis by Word; Sentiment Analysis by Message; N-gram Analysis; Summary; Bibliography; Index; About the Authors; Colophon.
- Chapter 7. Case Study: Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study: Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.