In recent times, the popularity of social media has grown exponentially and is increasingly being used as a channel for mass communication, such that the brands consider it as a medium of promotion and people largely use it for content sharing. With the increase in the number of users online, the data generated has increased many folds, bringing in the huge scope for gaining insights into the untapped gold mine, the social media data.
Mastering Social Media Mining with R will provide you with a detailed step-by-step guide to access the data using R and the APIs of various social media sites, such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and a few more networks. Most importantly, this book will provide you detailed explanations of implementation of various use cases using R programming; and by reading this book, you will be ready to embark your journey as an independent social media analyst. This book is structured in such a way that people new to the field of data mining or a seasoned professional can learn to solve powerful business cases with the application of machine learning techniques on the social media data.
Chapter 1, Fundaments of Mining, introduces you to the concepts of social media mining, various social media platforms, generic processes involved in accessing and processing the data, and techniques that can be implemented, as well as the importance, challenges, and applications of social media mining.
Chapter 2, Mining Opinions, Exploring Trends, and More with Twitter, focuses on steps involved in collecting tweets using the Twitter API and solve business cases, such as identifying the trending topics, searching tweets, collecting tweets, processing them, performing sentiment analysis, exploring few business cases based on sentiment analysis, and visualizing the sentiments in the form of word clouds.
Chapter 3, Find Friends on Facebook, discusses the usage of the Facebook API and uses the extracted data to measure click-through rate performance, detect spam messages, implement and explore the concepts of social graphs, and build recommendations using the Apriori algorithm on pages to like.
Chapter 4, Finding Popular Photos on Instagram, helps you understand the procedure involved in pulling the data using the Instagram API and helps you extract the popular personalities and destinations, building different types of clusters, and implementing recommendation engine based on the user-based collaborative filtering approach.
Chapter 5, Let's Build Software with GitHub, teaches you to use the GitHub API from R and also helps you understand the ways in which you can get the solutions to business questions by performing graphical and nongraphical exploration data analysis, which includes some basic charts, trend analysis, heat maps, scatter plots, and much more.
Chapter 6, More Social Media Websites, helps you understand the functioning of APIs of various social media websites and covers the business cases that can be solved.
In order to make your learning efficient, you need to have a computer with either Windows, Mac, or Ubuntu.
You need to download R to execute the codes mentioned in this book. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the codes are written using RStudio. RStudio is an integrated development environment for R and can be downloaded from http://www.rstudio.com/products/rstudio/.
In order to access the APIs of the social media, it will be necessary to create an app and follow certain instructions. All of these procedures are explained in their respective chapters.
Mastering Social Media Mining with R is intended for those who have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, or if you are a data analyst and interested in mining social media data; however, there is no need to have any prior knowledge of the usage of APIs of social media websites. This book will make you master in getting the required social media data and transforming them into actions resulting in improved business values.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."
A block of code is set as follows:
[default] post_id<- head(page$id, n = 100) head(post_id, n=10) post_id<- as.matrix(post_id)
Any command-line input or output is written as follows:
# Location (Country)
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."
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