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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
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Author (1):
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Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data A. Next Steps… Index

Function words


One of the earliest types of features, and one that still works quite well for authorship analysis, is to use function words in a bag-of-words model. Function words are words that have little meaning on their own, but are required for creating (English) sentences. For example, the words this and which are words that are really only defined by what they do within a sentence, rather than their meaning in themselves. Contrast this with a content word such as tiger, which has an explicit meaning and invokes imagery of a large cat when used in a sentence.

Function words are not always clearly clarified. A good rule of thumb is to choose the most frequent words in usage (over all possible documents, not just ones from the same author). Typically, the more frequently a word is used, the better it is for authorship analysis. In contrast, the less frequently a word is used, the better it is for content-based text mining, such as in the next chapter, where we look at the topic of different...

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