Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Table of Contents (22 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Sentiment analysis

The first kind of analysis is called sentiment analysis. It basically involves trying to understand the mood expressed in a piece of text. We are therefore going to look for the overall sentiment of each of the comments to see whether the general sentiment is mainly good or bad for those companies.

A common technique employed to perform this analysis is based on the use of a lexicon, which is a dataset that stores a wide list of words, with each word paired with an attribute that expresses the sentiment of the given word. The tidytext package provides three different lexicons to choose from:

  • afinn : Assigning the sentiment as a score from -5 (negative) to 5 (positive)
  • bing: Denoting the sentiment as either positive or negative
  • nrc: Assigning various levels of sentiment, such as joy and fear

We can easily explore them by calling the get_sentiments() function . Let's inspect bing:


What do we do now to understand the sentiment of our documents?

The most straightforward...