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Home > Data > Data Analysis > Python Social Media Analytics
Python Social Media Analytics
Python Social Media Analytics

Python Social Media Analytics: Analyze and visualize data from Twitter, YouTube, GitHub, and more

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Profile Icon Siddhartha Chatterjee Profile Icon Baihaqi Siregar Profile Icon Michal Krystyanczuk
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$29.99 $43.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (2 Ratings)
eBook Jul 2017 312 pages 1st Edition
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Profile Icon Siddhartha Chatterjee Profile Icon Baihaqi Siregar Profile Icon Michal Krystyanczuk
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (2 Ratings)
eBook Jul 2017 312 pages 1st Edition
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Python Social Media Analytics

Harnessing Social Data - Connecting, Capturing, and Cleaning

The first step to realize the promise of social data, which we went through in Chapter 1, Introduction to the Latest Social Media Landscape and Importance, is by harnessing it. A proper harnessing strategy can help to remove obstacles to and expedite processing. As we saw in the last chapter, many sources of social data can be used through the Application Protocol Interfaces (APIs) of these platforms. However, the data coming from APIs is not readily usable for multiple cases, hence it requires several steps before the data is ready to be analyzed and then applied. Therefore, we have dedicated a chapter that explains in detail how to do this. We have briefly touched upon the technical notion of an API in the first chapter. Here we intend to go deeper into it and help you to understand its types and usage. We also want...

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

...

Introduction to authentication techniques

Getting data from different APIs requires a good understanding of the data structure and authentication techniques that allow external users to access the resources served by an API. Historically, there were multiple ways of accessing API resources, but nowadays there is one common protocol used by all the main social media networks. When you get into the developer documentation you will most probably encounter the problem of authentication referred to by an enigmatic term, OAuth.

What is OAuth?

OAuth is simply an authorization protocol that allows users to share data with an application without sharing the password. It is a way to obtain a secure authorization scheme based on a token...

Parsing API outputs

The following subchapter will show you how to connect to different social networks and how to retrieve sample data.

Twitter

Twitter proposes three main APIs: the REST API, Streaming API, and the Ads API. We will be focused on the first two APIs, which provide respectively on-demand or stream data.

Creating application

As explained in the section about OAuth, you have to obtain credentials to be able to collect data from Twitter. There are some simple steps to perform this action:

  1. Create a Twitter account or use your existing one.
  2. Go to https://apps...

Basic cleaning techniques

Social media contains different types of data: information about user profiles, statistics (number of likes or number of followers), verbatims, and media. Quantitative data is very convenient for an analysis using statistical and numerical methods, but unstructured data such as user comments is much more challenging. To get meaningful information, one has to perform the whole process of information retrieval. It starts with the definition of the data type and data structure. On social media, unstructured data is related to text, images, videos, and sound and we will mostly deal with textual data. Then, the data has to be cleaned and normalized. Only after all these steps can we delve into the analysis.

Data type and encoding

...

MongoDB to store and access social data

According to Wikipedia:

MongoDB (from humongous) is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software.

Even though the objective of the chapter is not to explore the architecture of MongoDB in depth we are however going to explain the points that justify the use of MongoDB.

Along with ease of use, MongoDB is recognized for the following advantages:

  • Schema-less design: Unlike traditional relational databases, which require the data to fit its...

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

Currently, two types of API are available. They are as follows:

  • RESTful API
  • Stream API

RESTful API

This is the most common type of API that most social media provides. The information from a REST API is static and is from historical data. The...

Introduction to authentication techniques

Getting data from different APIs requires a good understanding of the data structure and authentication techniques that allow external users to access the resources served by an API. Historically, there were multiple ways of accessing API resources, but nowadays there is one common protocol used by all the main social media networks. When you get into the developer documentation you will most probably encounter the problem of authentication referred to by an enigmatic term, OAuth.

What is OAuth?

OAuth is simply an authorization protocol that allows users to share data with an application without sharing the password. It is a way to obtain a secure authorization scheme based on a token-based authorization mechanism. There are two API authentication models using OAuth:

  • User authentication
  • Application authentication

User authentication

This is the most common form of resource authentication implementation. The signed request both identifies an application...

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Key benefits

  • Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more
  • Analyze and extract actionable insights from your social data using various Python tools
  • A highly practical guide to conducting efficient social media analytics at scale

Description

Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.

Who is this book for?

If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process.

What you will learn

  • Understand the basics of social media mining
  • Use PyMongo to clean, store, and access data in MongoDB
  • Understand user reactions and emotion detection on Facebook
  • Perform Twitter sentiment analysis and entity recognition using Python
  • Analyze video and campaign performance on YouTube
  • Mine popular trends on GitHub and predict the next big technology
  • Extract conversational topics on public internet forums
  • Analyze user interests on Pinterest
  • Perform large-scale social media analytics on the cloud

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 28, 2017
Length: 312 pages
Edition : 1st
Language : English
ISBN-13 : 9781787126756
Category :
Data
Languages :
Python
Concepts :
Data Analysis

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Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
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Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Jul 28, 2017
Length: 312 pages
Edition : 1st
Language : English
ISBN-13 : 9781787126756
Category :
Data
Languages :
Python
Concepts :
Data Analysis

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Table of Contents

9 Chapters
Introduction to the Latest Social Media Landscape and Importance Chevron down icon Chevron up icon
Introduction to the Latest Social Media Landscape and Importance
Introducing social graph
Delving into social data
Understanding the process
Working environment
Getting the data
Analyzing the data
Visualizing the data
Getting started with the toolset
Summary
Harnessing Social Data - Connecting, Capturing, and Cleaning Chevron down icon Chevron up icon
Harnessing Social Data - Connecting, Capturing, and Cleaning
APIs in a nutshell
Introduction to authentication techniques
Parsing API outputs
Basic cleaning techniques
MongoDB to store and access social data
MongoDB using Python
Summary
Uncovering Brand Activity, Popularity, and Emotions on Facebook Chevron down icon Chevron up icon
Uncovering Brand Activity, Popularity, and Emotions on Facebook
Facebook brand page
Project planning
Analysis
Keywords
Noun phrases
Detecting trends in time series
Uncovering emotions
How can brands benefit from it?
Summary
Analyzing Twitter Using Sentiment Analysis and Entity Recognition Chevron down icon Chevron up icon
Analyzing Twitter Using Sentiment Analysis and Entity Recognition
Scope and process
Getting the data
Sentiment analysis
Customized sentiment analysis
Named entity recognition
Combining NER and sentiment analysis
Summary
Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured Chevron down icon Chevron up icon
Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
The Next Great Technology – Trends Mining on GitHub Chevron down icon Chevron up icon
The Next Great Technology – Trends Mining on GitHub
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
Scraping and Extracting Conversational Topics on Internet Forums Chevron down icon Chevron up icon
Scraping and Extracting Conversational Topics on Internet Forums
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
Demystifying Pinterest through Network Analysis of Users Interests Chevron down icon Chevron up icon
Demystifying Pinterest through Network Analysis of Users Interests
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
Social Data Analytics at Scale – Spark and Amazon Web Services Chevron down icon Chevron up icon
Social Data Analytics at Scale – Spark and Amazon Web Services
Different scaling methods and platforms
Topic models at scale
Spark on the Cloud – Amazon Elastic MapReduce
Summary

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Customer reviews

Rating distribution
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Neha Aug 14, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A really good book for a fresher who wants to study social meadia analytics.
Amazon Verified review Amazon
casatishum Dec 12, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
some codes no longer works since social media tend to change fast. on top of that, there're also errors in code, which makes it confusing and frustrating to follow. not recommending.
Amazon Verified review Amazon

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About the 3 authors

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Profile icon 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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Profile icon Baihaqi Siregar
Baihaqi Siregar
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Profile icon 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.
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