Book Image

Mastering Text Mining with R

By : KUMAR ASHISH
Book Image

Mastering Text Mining with R

By: KUMAR ASHISH

Overview of this book

Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.
Table of Contents (15 chapters)

Kernel methods


Kernel methods exploit the similarity between documents, that is, by length, topic, language, and so on, to extract patterns from the documents. Inner products between data items can reveal a lot of latent information; in fact many of the standard algorithms can be represented in the form of inner products between data items in a potentially complex feature space. The reason why kernel methods are suitable for high dimensional data is that the complexity only depends on the choice of kernel, it does not depend upon the features of the data in use. Kernels solve the computational issues by transforming the data into richer feature spaces and non-linear features and then applying linear classifier to the transformed data, as shown in the following diagram:

Some of the kernel methods available are:

  • Linear kernel

  • Polynomial kernel

  • Radical base function kernel

  • Sigmoid kernel

Support vector machines

Support vector machines (SVM) is a kernel method of classification, which gained a lot...