Text Mining with R
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.
Table of contents
- The Tidy Text Format
- Sentiment Analysis with Tidy Data
- Analyzing Word and Document Frequency: tf-idf
- Relationships Between Words: N-grams and Correlations
- Converting to and from Nontidy Formats
- Topic Modeling
- Case Study: Comparing Twitter Archives
- Case Study: Mining NASA Metadata
- Case Study: Analyzing Usenet Text
| Pages : | 194 |
| Size : | |
| File type : | |
| Downloads: | 145 |
| Created: | 2022-02-03 |
| License: | CC BY-NC-SA |
| Author(s): | Julia Silge, David Robinson |
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