The digital era has seen a humongous increase in data, which is unstructured and needs to be processed to extract any information out of it. Research in the field of natural language processing has paved the way towards automatic organization and classification of documents into the categories that they belong to. Document classification finds its utility in numerous applications such as spam classification, mail routing, priority inbox or mail relevance ranking, news monitoring and censoring, identification or article genre, and indexing of documents. The text classification process flow is described in the following diagram. We have discussed the preprocessing steps in Chapter 5, Text Summarization and Clustering, which involves basic data cleansing. After this step, we choose the document representation method. Features extraction and selection is performed on the cleansed data as per the document representation method chosen in the last step. We have discussed feature...
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.
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