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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


Harnessing social data is of vital importance for any worthwhile application. Public data from social media APIs is messy, noisy, and voluminous, and requires a precise and smart strategy to keep the surface away from the noise. The first step in harnessing social data is to collect it by following the steps to connect it to various RESTful APIs and following authentication techniques. Each social network has variations of its API but the basic rules of app creation and authentication remain a common method. Once we successfully make connection to an API we need to parse the JSON data that is collected. The data arriving at the programmers end through the APIs need to be cleaned through basic text mining such as tokenization, duplicate removal, and normalization techniques. Social media data is often unstructured and in various formats, so traditional relational databases are not suitable for these use cases. Finally, we need a flexible and scalable system to stock thousands of social...

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