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)

Latent semantic analysis


Latent Semantic Analysis (LSA) is a modeling technique that can be used to understand a given collection of documents. It also provides us with insights into the relationship between words in the documents, unravels the concealed structure in the document contents, and creates a group of suitable topics - each topic has information about the data variation that explains the context of the corpus. This modeling technique can come in handy in a variety of natural language processing or information retrieval tasks. LSA can filter out the noise features in the data and represent the data in a simpler form, and discover topics with high affinity.

The topics that are extracted from the collection of documents have the following properties:

  • The amount of similarity each topic has with each document in the corpus.

  • The amount of similarity each topic has with each term in the corpus.

  • It also provides a significance score that highlights the importance of the topic and the variance...