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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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Table of Contents (17) Chapters Close

Title Page
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Customer Feedback
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


Facebook has become the de facto place for brands all over the world to communicate about their products, offers, and news. Not at all surprising considering that there are more than a billion users and consumers on the social media. Unlike in traditional media, on Facebook, not just the brand has a voice but also the consumers which in return generates a lot of engagement. The goal of this chapter was to show a glimpse of how to get interesting insights into the activities of Facebook pages of brands without getting lost in all the content. To analyze exhaustively all the content is beyond the scope of this chapter. We chose Google's brand page as an example for the analysis and have demonstrated how to collect, process, and visualize the data from Facebook and measure engagements. Extracting the top keywords, hashtags and noun phrases allowed us to understand the most important content of both the brand and the users. We have also shown how to extract emotions in content of both...

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