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Building Machine Learning Systems with Python

You're reading from  Building Machine Learning Systems with Python

Product type Book
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Pages 290 pages
Edition 1st Edition
Languages

Table of Contents (20) Chapters

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning 2. Learning How to Classify with Real-world Examples 3. Clustering – Finding Related Posts 4. Topic Modeling 5. Classification – Detecting Poor Answers 6. Classification II – Sentiment Analysis 7. Regression – Recommendations 8. Regression – Recommendations Improved 9. Classification III – Music Genre Classification 10. Computer Vision – Pattern Recognition 11. Dimensionality Reduction 12. Big(ger) Data Where to Learn More about Machine Learning Index

Chapter 6. Classification II – Sentiment Analysis

For companies, it is vital to closely monitor the public reception of key events such as product launches or press releases. With real-time access and easy accessibility of user-generated content on Twitter, it is now possible to do sentiment classification of tweets. Sometimes also called opinion mining, it is an active field of research in which several companies are already selling their products. As this shows that a market obviously exists, we have motivation to use our classification muscles built in the previous chapter to build our own home-grown sentiment classifier.

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