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Python Social Media Analytics

You're reading from   Python Social Media Analytics Analyze and visualize data from Twitter, YouTube, GitHub, and more

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787121485
Length 312 pages
Edition 1st Edition
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Authors (3):
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Baihaqi Siregar Baihaqi Siregar
Author Profile Icon Baihaqi Siregar
Baihaqi Siregar
Siddhartha Chatterjee Siddhartha Chatterjee
Author Profile Icon Siddhartha Chatterjee
Siddhartha Chatterjee
Michal Krystyanczuk Michal Krystyanczuk
Author Profile Icon Michal Krystyanczuk
Michal Krystyanczuk
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to the Latest Social Media Landscape and Importance 2. Harnessing Social Data - Connecting, Capturing, and Cleaning FREE CHAPTER 3. Uncovering Brand Activity, Popularity, and Emotions on Facebook 4. Analyzing Twitter Using Sentiment Analysis and Entity Recognition 5. Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured 6. The Next Great Technology – Trends Mining on GitHub 7. Scraping and Extracting Conversational Topics on Internet Forums 8. Demystifying Pinterest through Network Analysis of Users Interests 9. Social Data Analytics at Scale – Spark and Amazon Web Services

Facebook brand page


The best way to familiarize ourselves with the brand activity on Facebook is to check out the actual brand page on Facebook. If you use Facebook, any brand page can be accessed using the search box, as we've done for the case of Google in the following screenshot. We will eventually extract data for from this page using the Facebook API for our analysis.

As shown in the following screenshot, there is content that is posted by both Google and its fans:

The Facebook API

As explained in Chapter 2, Harnessing Social Data - Connecting, Capturing, and Cleaning, the Facebook API consists of multiple functionalities. For the purpose of this chapter, we will use the Facebook Graph API.

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