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Learning Data Mining with Python, - Second Edition

You're reading from  Learning Data Mining with Python, - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Pages 358 pages
Edition 2nd Edition
Languages
Concepts

Table of Contents (20) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Data Mining 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Chapter 9. Authorship Attribution

Authorship analysis is a text mining task that aims to identify certain aspects about an author, based only on the content of their writings. This could include characteristics such as age, gender, or background. In the specific authorship attribution task, we aim to identify which of a set of authors wrote a particular document. This is a classic classification task. In many ways, authorship analysis tasks are performed using standard data mining methodologies, such as cross-fold validation, feature extraction, and classification algorithms.

In this chapter, we will use the problem of authorship attribution to piece together the parts of the data mining methodology we developed in the previous chapters. We identify the problem and discuss the background and knowledge of the problem. This lets us choose features to extract, which we will build a pipeline for achieving. We will test two different types of features: function words and character n -grams. Finally...

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