Python Social Media Analytics

By : Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

4 (2)

Buy this Book

Python Social Media Analytics

4 (2)

By: Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

Buy this Book

Overview of this book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

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

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

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

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

Scope and process

Getting the data

Data pull

Data processing

Data analysis

Summary

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

Scope and process

Getting the data

Data pull and pre-processing

Data analysis

Summary

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

Different scaling methods and platforms

Topic models at scale

Spark on the Cloud – Amazon Elastic MapReduce

Summary

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What you need for this book

The goal of the book is to explain the concept of social media analytics and demonstrate its applications using Python. We use Python 3 for the different concepts and projects in the book. You will need a Linux/macOS or Windows machine with Python 3 and an IDE of your choice (Sublime Text 2, Atom, or gedit). All the libraries presented in the chapters can be easily installed with pip package manager. It is advisable to use the Python library Jupyter to work in notebook mode.

The data will be stored in MongoDB, which is compatible with all operating systems. You can follow the installation instruction on the official website (https://www.mongodb.com).

Lastly, a good internet connection is a must to be able to process big volumes of data from social networks.