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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

Analysis

Now that we have defined precisely the process and the techniques that we apply, we will move to the actual exercise of generating the code for the data analysis life cycle of extracting the data, storing it, cleaning it, and applying text mining techniques to analyze the content on the Facebook page of both Google and its users.

Step 1 – data extraction

In the first step, we will define Facebook endpoints, which will be used to retrieve the data from Facebook. We need two different endpoints in order to be able to extract all the posts and comments from the Google Facebook page. Creation of an access token was explained in Chapter 2, Harnessing Social Data - Connecting, Capturing, and Cleaning and it is a...

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