| Description |
1 online resource : illustrations (black and white). |
| Series |
Chapman & Hall/CRC data mining and knowledge discovery series |
|
Chapman & Hall/CRC data mining and knowledge discovery series.
|
| Contents |
Front Cover; Contents; I: RapidMiner; 1. RapidMiner for Text Analytic Fundamentals; 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents; II: KNIME; 3. Introduction to the KNIME Text Processing Extension; 4. Social Media Analysis -- Text Mining Meets Network Mining; III: Python; 5. Mining Unstructured User Reviews with Python; 6. Sentiment Classification and Visualization of Product Review Data; 7. Mining Search Logs for Usage Patterns; 8. Temporally Aware Online News Mining and Visualization with Python; 9. Text Classification Using Python; IV: R. |
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10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool11. Topic Modeling; 12. Empirical Analysis of the Stack Overflow Tags Network; Back Cover. |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a w. |
| Subject |
Data mining.
|
|
Data Mining |
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Exploration de données (Informatique) |
|
Data mining |
| Added Author |
Hofmann, Markus (Computer scientist), editor.
|
|
Chisholm, Andrew, editor.
|
| Other Form: |
Print version: 9781482237573 |
| ISBN |
9781482237580 (PDF ebook) |
|
148223758X (PDF ebook) |
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(hbk.) |
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