Book Image

Python Social Media Analytics

By : Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk
Book Image

Python Social Media Analytics

By: Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

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.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
Acknowledgments
Acknowledgments
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Introduction to the Latest Social Media Landscape and Importance
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
2
Harnessing Social Data - Connecting, Capturing, and Cleaning
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
3
Uncovering Brand Activity, Popularity, and Emotions on Facebook
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
4
Analyzing Twitter Using Sentiment Analysis and Entity Recognition
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
5
Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
6
The Next Great Technology – Trends Mining on GitHub
The Next Great Technology – Trends Mining on GitHub
Scope and process
Getting the data
Data pull
Data processing
Data analysis
Summary
7
Scraping and Extracting Conversational Topics on Internet Forums
Scraping and Extracting Conversational Topics on Internet Forums
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
8
Demystifying Pinterest through Network Analysis of Users Interests
Demystifying Pinterest through Network Analysis of Users Interests
Scope and process
Getting the data
Data pull and pre-processing
Data analysis
Summary
9
Social Data Analytics at Scale – Spark and Amazon Web Services
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

Data analysis

As the data is ready, we can start our analysis. We'll explore time-series analysis for analyzing our dataset using the features that we have extracted. Our goal is to answer the following questions:

  • How does sentiment evolve over time? Are there any peaks?
  • What are the periods when users comment more?
  • What is the day of week when users put the most positive comments?
  • What is the day of week when they comment most actively?

In order to answer these questions, we need to work on data in time. Pandas dataframe structures have many interesting properties for time series handling. In previous steps, we have changed the index in our data frame by setting it to the datetime object. We can now use it to resample our data according to the time frame.

Sentiment analysis in time

Firstly, we'll compute the average sentiment score per month:

df['sentiment'].resample('M').mean()

The argument of the resample() method indicates time window. We could also use other periods, for example:

  • B: Business...
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