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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

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781787121485

Length 312 pages

Edition 1st Edition

Languages

Python

Tools

GitHub

Concepts

Data Analysis

Authors (3):

Baihaqi Siregar

Siddhartha Chatterjee

Michal Krystyanczuk

View More author details

Table of Contents (10) Chapters

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

Getting the data

YouTube provides very powerful APIs that can be used for interacting with videos or just getting information about them. The various numbers of APIs, endpoints, and parameters are a good selection of tools for creating applications and for getting informative data for further analysis. To follow the title of the chapter, we will be interested in the data that can bring valuable information about brands or products.

The Google documentation regarding the YouTube API is really large; hence, we will focus only on some parts of it to get started with this API, easily and rapidly. For our objective, we will use the YouTube Data API.

We are going to make authorized HTTP requests to the YouTube API www.googleapis.com/youtube/v3 for retrieving data.

The endpoint has several resources (methods). For our case, the methods /videos and /commentThreads will return sufficient amounts of data.

Like other APIs, the YouTube API requires an authorization. Basically, when the user requests some...

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Authors (3)

Siddhartha Chatterjee

Siddhartha Chatterjee

Siddhartha Chatterjee is an experienced data scientist with a strong focus in the area of machine learning and big data applied to digital (e-commerce and CRM) and social media analytics. He worked between 2007 to 2012 with companies such as IBM, Cognizant Technologies, and Technicolor Research and Innovation. He completed a Pan-European Masters in Data Mining and Knowledge Management at Ecole Polytechnique of the University of Nantes and University of Eastern Piedmont, Italy. Since 2012, he has worked at OgilvyOne Worldwide, a leading global customer engagement agency in Paris, as a lead data scientist and set up the social media analytics and predictive analytics offering. From 2014 to 2016, he was a senior data scientist and head of semantic data of Publicis, France. During his time at Ogilvy and Publicis, he worked on international projects for brands such as Nestle, AXA, BNP Paribas, McDonald's, Orange, Netflix, and others. Currently, Siddhartha is serving as head of data and analytics of Groupe Aéroport des Paris.

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Baihaqi Siregar

Baihaqi Siregar

See other products by Baihaqi Siregar

Michal Krystyanczuk

Michal Krystyanczuk

Michal Krystyanczuk is the co-founder of The Data Strategy, a start-up company based in Paris that builds artificial intelligence technologies to provide consumer insights from unstructured data. Previously, he worked as a data scientist in the financial sector using machine learning and big data techniques for tasks such as pattern recognition on financial markets, credit scoring, and hedging strategies optimization. He specializes in social media analysis for brands using advanced natural language processing and machine learning algorithms. He has managed semantic data projects for global brands, such as Mulberry, BNP Paribas, Groupe SEB, Publicis, Chipotle, and others. He is an enthusiast of cognitive computing and information retrieval from different types of data, such as text, image, and video.

See other products by Michal Krystyanczuk

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