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Machine Learning for the Web

You're reading from   Machine Learning for the Web Gaining insight and intelligence from the internet with Python

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781785886607
Length 298 pages
Edition 1st Edition
Languages
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Authors (2):
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Andrea Isoni Andrea Isoni
Author Profile Icon Andrea Isoni
Andrea Isoni
Steve Essinger Steve Essinger
Author Profile Icon Steve Essinger
Steve Essinger
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Practical Machine Learning Using Python FREE CHAPTER 2. Unsupervised Machine Learning 3. Supervised Machine Learning 4. Web Mining Techniques 5. Recommendation Systems 6. Getting Started with Django 7. Movie Recommendation System Web Application 8. Sentiment Analyser Application for Movie Reviews Index

Web content mining


This type of mining focuses on extracting information from the content of web pages. Each page is usually gathered and organized (using a parsing technique), processed to remove the unimportant parts from the text (natural language processing), and then analyzed using an information retrieval system to match the relevant documents to a given query. These three components are discussed in the following paragraphs.

Parsing

A web page is written in HTML format, so the first operation is to extract the relevant pieces of information. An HTML parser builds a tree of tags from which the content can be extracted. Nowadays, there are many parsers available, but as an example, we use the Scrapy library see Chapter 7, Movie Recommendation System Web Application which provides a command-line parser. Let's say we want to parse the main page of Wikipedia, https://en.wikipedia.org/wiki/Main_Page. We simply type this in a terminal:

scrapy shell 'https://en.wikipedia.org/wiki/Main_Page...
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