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  • Book Overview & Buying Mastering Social Media Mining with Python
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Mastering Social Media Mining with Python

Mastering Social Media Mining with Python

By : Marco Bonzanini
4.7 (6)
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Mastering Social Media Mining with Python

Mastering Social Media Mining with Python

4.7 (6)
By: Marco Bonzanini
Buy this Book

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)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Social Media, Social Data, and Python
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1. Social Media, Social Data, and Python
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Getting started
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Social media - challenges and opportunities
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Python tools for data science
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Processing data in Python
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Building complex data pipelines
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Summary
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2. #MiningTwitter – Hashtags, Topics, and Time Series
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2. #MiningTwitter – Hashtags, Topics, and Time Series
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Getting started
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The Twitter API
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Collecting data from Twitter
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Analyzing tweets - entity analysis
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Analyzing tweets - text analysis
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Analyzing tweets - time series analysis
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Summary
3
3. Users, Followers, and Communities on Twitter
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3. Users, Followers, and Communities on Twitter
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Users, friends, and followers
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Mining your followers
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Mining the conversation
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Plotting tweets on a map
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Summary
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4. Posts, Pages, and User Interactions on Facebook
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4. Posts, Pages, and User Interactions on Facebook
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The Facebook Graph API
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Mining your posts
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Mining Facebook Pages
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Summary
5
5. Topic Analysis on Google+
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5. Topic Analysis on Google+
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Getting started with the Google+ API
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Embedding the search results in a web GUI
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Notes and activities from a Google+ page
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Text analysis and TF-IDF on notes
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Summary
6
6. Questions and Answers on Stack Exchange
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6. Questions and Answers on Stack Exchange
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Questions and answers
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Getting started with the Stack Exchange API
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Working with Stack Exchange data dumps
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Text classification for question tags
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Summary
7
7. Blogs, RSS, Wikipedia, and Natural Language Processing
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7. Blogs, RSS, Wikipedia, and Natural Language Processing
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Blogs and NLP
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Getting data from blogs and websites
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NLP Basics
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Summary
8
8. Mining All the Data!
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8. Mining All the Data!
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Many social APIs
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Mining videos on YouTube
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Mining open source software on GitHub
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Mining local businesses on Yelp
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Building a custom Python client
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Summary
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9. Linked Data and the Semantic Web
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9. Linked Data and the Semantic Web
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A Web of Data
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Mining relations from DBpedia
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Mining geo coordinates
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Summary

NLP Basics

This section tries to scratch the surface of the complex field of NLP. The previous chapters have mentioned some of the basics that are necessary for dealing with textual data (for example, tokenization) without going too much into the details. Here, we'll try to go one step further into the basic understanding of this discipline. Due to its complexity and many aspects, we're taking a pragmatic approach and only scratching the surface of the theoretical foundations in favor of practical examples.

Text preprocessing

An essential part of any NLP system is the preprocessing pipeline. Before we can perform any interesting task on a piece of text, we must first convert it in a useful representation.

In the previous chapters, we already performed some analysis on the textual data without digging into the details of text preprocessing, but instead using the common tools with a pragmatic approach. In this section, we'll highlight some of the common preprocessing steps and discuss their role...

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