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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

Data pull

We start the analysis by selecting the channel and the video. For the purpose of this chapter we select the Sony PlayStation YouTube channel, as it is one of the most popular brands in the entertainment sector. We find its URL by performing a search on the YouTube search engine and get a result similar to the following: https://www.youtube.com/channel/UC-2Y8dQb0S6DtpxNgAKoJKA

The ID of the channel corresponds to the last element of the URL, which in this case is UC-2Y8dQb0S6DtpxNgAKoJKA.

In the first place, we will extract the videos with the most views to understand what kind of videos gather most interest from users. In order to perform this task we have to define two functions:

get_channel_videos(): A function to list all the videos associated with the channel
get_statistics(): A function that collects statistics (number of views, likes, dislikes) for a single video

It is required to split this part in two steps as there is no endpoint that retrieves all the videos for a channel...

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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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