Book Image

R Data Analysis Projects

Book Image

R Data Analysis Projects

Overview of this book

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.
Table of Contents (15 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Sentiment classification


Sentiment in text can be either positive or negative. We will stick to this definition for this chapter. Sentiment mining is an active field of research. A starting point for someone to learn the various aspects of sentiment mining is through the book Sentiment Analysis and Opinion Mining, by Bing Liu.

Broadly speaking, sentiment mining problems are solved using the following techniques:

Dictionary methods

Sentiment lexicons, in which words are categorized as positive and negative, are used in this technique. There are several lexicons available today:

  • SocialSent is a domain specific lexicon available from Stanford University: https://nlp.stanford.edu/projects/socialsent/
  • Wordnet is a lexical database for English from Princeton: http://wordnet.princeton.edu/
  • There are several sentiment annotated datasets available at Bing Liu's website: https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

We match these lexicons to the tokens (words) in the input data and we can then...