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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
Languages
Tools
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 2. Data Acquisition and Extraction FREE CHAPTER 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

Determining and removing stop words

Stop words are common words that, in a natural language processing situation, do not provide much contextual meaning. These words are often the most common words in a language. These tend to, at least in English, be articles and pronouns, such as I, me, the, is, which, who, at, among others. Processing of meaning in documents can often be facilitated by removal of these words before processing, and hence many tools support this ability. NLTK is one of these, and comes with support for stop word removal for roughly 22 languages.

How to do it

Proceed with the recipe as follows (code is available in 07/06_freq_dist.py):

  1. The following demonstrates stop word removal using NLTK. First,...
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