Text classification is an extensively used phenomenon in natural language processing which has widespread utility in the different domains. Also known as text categorization, text classification finds its usage in various tasks related to information retrieval and management. Spam detection in e-mails, opinion mining or sentiment analysis on social media data, priority e-mail sorting, intent identification from user queries in chatbots, and automated query answering mechanisms are a few examples where text categorization has proved to be highly effective. In earlier chapters, we have discussed various feature selection and dimensionality reduction methods, which are preprocessing steps before text classification. We will briefly discuss supervised learning or classification mechanisms, how a learner is designed, and then we will move on to their implementation in terms of text data. We will also discuss the different cross-validation and evaluation mechanisms...
Mastering Text Mining with R
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Mastering Text Mining with R
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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.
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Mastering Text Mining with R
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Table of Contents (15 chapters)
Mastering Text Mining with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Statistical Linguistics with R
Processing Text
Categorizing and Tagging Text
Dimensionality Reduction
Text Summarization and Clustering
Text Classification
Entity Recognition
Index
Customer Reviews