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Python Web Scraping Cookbook

You're reading from   Python Web Scraping Cookbook Over 90 proven recipes to get you scraping with Python, microservices, Docker, and AWS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787285217
Length 364 pages
Edition 1st Edition
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Concepts
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Scraping FREE CHAPTER 2. Data Acquisition and Extraction 3. Processing Data 4. Working with Images, Audio, and other Assets 5. Scraping - Code of Conduct 6. Scraping Challenges and Solutions 7. Text Wrangling and Analysis 8. Searching, Mining and Visualizing Data 9. Creating a Simple Data API 10. Creating Scraper Microservices with Docker 11. Making the Scraper as a Service Real 12. Other Books You May Enjoy

Performing stemming

Stemming is the process of cutting down a token to its stem. Technically, it is the process or reducing inflected (and sometimes derived) words to their word stem - the base root form of the word. As an example, the words fishing, fished, and fisher stem from the root word fish. This helps to reduce the set of words being processed into a smaller base set that is more easily processed.

The most common algorithm for stemming was created by Martin Porter, and NLTK provides an implementation of this algorithm in the PorterStemmer. NLTK also provides an implementation of a Snowball stemmer, which was also created by Porter, and designed to handle languages other than English. There is one more implementation provided by NLTK referred to as a Lancaster stemmer. The Lancaster stemmer is considered the most aggressive stemmer of the three.

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