Text mining and visualization : case studies using open-source tools / edited by Markus Hofmann, Andrew Chisholm.
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...
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| Online Access: |
Full Text (via Taylor & Francis) |
|---|---|
| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton :
Chapman & Hall/CRC,
2016.
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| Edition: | 1st. |
| Series: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Subjects: |
MARC
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| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
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| 700 | 1 | |a Chisholm, Andrew, |e editor. | |
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