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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

APIs in a nutshell

An API is the medium that allows the exchange of data points between a service and the programmer or user. API concepts have been widely used in the software industry when we needed different software to exchange data with with another. Mobile and internet applications have been using web services and APIs to enrich information from external sources. Social media also started creating APIs to share their data with third-party application developers. The popularity of data science has made APIs emerge also as a source for mining and knowledge creation. The nature of all social media is different, so are their APIs. The steps involved in making a connection may not differ greatly, but the data points we capture do.

Different types of API

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