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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


Conventional social media such as Facebook and Twitter with user-generated content are an interesting way to analyze information about individuals and organizations. The APIs of these platforms are useful to gather and analyse a lot of this data emanating from these platforms. on the other hand, online forums without structured freely available APIs provide a great sources of topical discussions. The main difference is mostly the anonymous nature of the forums, which encourages individuals to have long and deep discussions on various topics (technology, politics, culture, and so on), unlike on other social media where one is often identified through pseudonyms and actual identifiers. These millions of rich conversations on Internet forums have become extremely interesting for companies to learn about the trends of their consumers.

To harness conversational data, we explored the techniques to create crawlers using the Scrappy framework. After extraction and storage of this data, we...

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