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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 FREE CHAPTER 2. Harnessing Social Data - Connecting, Capturing, and Cleaning 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

Data analysis


Once we've captured and structured the two kinds of data on Pinterest, we want to use them to find answer, to three main topics:

  • Understand what are the most important topics in our own Pins
  • Find influencers on a topic in the search results
  • Find communities on a topic in the search results

Understanding relationships between our own topics

The aim of this part of the analysis is to answer a few questions about the topics that we are interested in on Pinterest:

  • What are the most important topics (central or main topic) by direct connections?
  • What are the topics which are most connected with others?
  • What are the topic hubs? Or how important is the topic in terms of connecting other topics?
  • What are the most important topics (central or main topic) by indirect connections?

To answer the preceding questions we are going to use a concept in graph theory and network analysis called centrality.

Centrality is essentially metrics that allow you to identify the most important vertices or nodes...

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