Python Social Media Analytics

Leverage the power of Python to collect, process, and mine deep insights from social media data
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Python Social Media Analytics

Siddhartha Chatterjee, Michal Krystyanczuk

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Leverage the power of Python to collect, process, and mine deep insights from social media data
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Book Details

ISBN 139781787121485
Paperback312 pages

Book 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.

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
Chapter 6: The Next Great Technology – Trends Mining on GitHub
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
Chapter 7: Scraping and Extracting Conversational Topics on Internet Forums
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
Chapter 8: Demystifying Pinterest through Network Analysis of Users Interests
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
Chapter 9: 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

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

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
Chapter 6: The Next Great Technology – Trends Mining on GitHub
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
Chapter 7: Scraping and Extracting Conversational Topics on Internet Forums
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
Chapter 8: Demystifying Pinterest through Network Analysis of Users Interests
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
Chapter 9: 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

Book Details

ISBN 139781787121485
Paperback312 pages
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