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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

Summary

The last decade has seen an enormous growth of social media platforms, such as Facebook, Twitter, and Youtube. Since 2009, another platform with a different format and objective has grown - Pinterest. Unlike conventional social media, which is used as a communication tool, Pinterest is described as a "catalog of ideas". It allows users to create pinboards, and to organize and share content over the web, based on interests and ideas.

We've explored the use of the Pinterest API and also advanced scraping techniques, using Selenium and BeautifulSoup, to gather data for learning purposes. However, these have time constraints to be scalable. Therefore, we extracted the data from our own pinboard using the endpoints (user, board, and pins) and the search results on the topic of fashion. For data analysis, we used bigram analysis to extract a list of topics on...

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