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Mastering Social Media Mining with Python

You're reading from   Mastering Social Media Mining with Python Unearth deeper insight from your social media data with advanced Python techniques for acquisition and analysis

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781783552016
Length 338 pages
Edition 1st Edition
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Author (1):
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Marco Bonzanini Marco Bonzanini
Author Profile Icon Marco Bonzanini
Marco Bonzanini
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Table of Contents (10) Chapters Close

Preface 1. Social Media, Social Data, and Python FREE CHAPTER 2. #MiningTwitter – Hashtags, Topics, and Time Series 3. Users, Followers, and Communities on Twitter 4. Posts, Pages, and User Interactions on Facebook 5. Topic Analysis on Google+ 6. Questions and Answers on Stack Exchange 7. Blogs, RSS, Wikipedia, and Natural Language Processing 8. Mining All the Data! 9. Linked Data and the Semantic Web

Plotting tweets on a map

This section discusses the visual representation of tweets using maps. Data visualizations are a nice way to provide an easy-to-digest overview of the data as a picture can provide a summary of a particular feature of a dataset.

In a small portion of tweets, we can find details about the geographic localization of the user's device in the form of geographic coordinates. While many users disable this functionality on their mobile, there is still an interesting opportunity in terms of data mining to understand how the tweets are geographically distributed.

This section introduces GeoJSON, a common data format for geographic data structures and the process of building interactive maps of our tweets.

From tweets to GeoJSON

GeoJSON (http://geojson.org) is a JSON-based format for encoding geographic data structures. A GeoJSON object can represent a geometry, feature, or collection of features. Geometries only contain the information about the shape; its examples include...

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