Book Image

Mastering Social Media Mining with Python

By : Marco Bonzanini
Book Image

Mastering Social Media Mining with Python

By: Marco Bonzanini

Overview of this book

Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.
Table of Contents (10 chapters)

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...