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 covered the procedure involved in creation of an app on the Instagram platform. We covered the sequential steps for authentication and accessing the data from R using the package instaR. We also acquired competency to build a dataset of users and location from the Instagram platform.

We discussed the skill of using the collected data to solve critical business problems. Some of the problems that we have solved include identifying the popular users based on multiple metrics, exploring the destinations which people talk about the most, dividing the dataset into different groups by applying a clustering algorithm, building a recommendation system using the collaborative filtering algorithm on who the users might be interested to follow based on the behavior of similar users, and finally a quick brief about the various other business cases that could be solved using the Instagram data.

In the next chapter we will be learning about the implementation of some of the graphical...