Book Image

Mastering Social Media Mining with R

Book Image

Mastering Social Media Mining with R

Overview of this book

With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst.
Table of Contents (13 chapters)
Mastering Social Media Mining with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


In this chapter, we tried to familiarize the user with the concept of social media and mining.

We discussed the OAuth API, which offers a technique for clients to allow third-party entry to their resources without sharing their credentials. It also offers a way to grant controlled access in terms of scope and duration.

We saw examples of various R packages available to visualize the text data. We discussed innovative ways to analyze and study the text data via plots. The application of sentiment analysis along with topic mining was also discussed in the same sections. To many, it's a new way to look at these kinds of data. Historically, people have used plots to plot numerical data, but plotting words on 2D graphs is very new. People have made more advances than 2D plots. With Facebook and LinkedIn, the Gephi library allows visualizing the social networks in 3D.

Next, you learned the basic steps of any data-mining problem along with various machine learning algorithms. We'll see the applications of many of these algorithms in the coming chapters. We briefly talked about sentiment analysis, anomaly detection, and various community detection algorithms. So far, we have not gone deep into any of the algorithms, but will dive into them in the later chapters.

In the next chapter, we will apply the knowledge gained so far to mine Twitter and give detailed information of the methods and techniques used there.