Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer

Sentiment analysis upon Tweets

Now that we are equipped with the key terms and concepts from the world of Sentiment Analysis, let us put our theory to the test. We have seen some major application areas for Sentiment Analysis and the challenges faced, in general, to perform such analytics. In this section we will perform Sentiment Analysis categorized into:

  • Polarity analysis: This will involve the scoring and aggregation of sentiment polarity using a labeled list of positive and negative words.

  • Classification-based analysis: In this approach we will make use of R's rich libraries to perform classification based on labeled tweets available for public usage. We will also discuss their performance and accuracy.

R has a very robust library for the extraction and manipulation of information from Twitter called TwitteR. As we saw in the previous chapter, we first need to create an application using Twitter's application management console before we can use TwitteR or any other library for sentiment...