Synopsis "Mastering Text Mining With R (in English)"
About This BookDevelop all the relevant skills for building text-mining apps with R with this easy-to-follow guideGain in-depth understanding of the text mining process with lucid implementation in the R languageExample-rich guide that lets you gain high-quality information from text dataWho This Book Is ForIf you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful.What You Will LearnGet acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining processAccess and manipulate data from different sources such as JSON and HTTPProcess text using regular expressionsGet to know the different approaches of tagging texts, such as POS tagging, to get started with text analysisExplore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in RDiscover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA)Build a baseline sentenceIn DetailThis book will help you develop a thorough understanding of the steps in the text mining process and gain confidence in applying the concepts to build text-data driven products.Starting with basic information about the statistics concepts used in text mining, the book will teach you how to access, cleanse, and process text using the R language and teach you how to analyze them. It 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, the 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.
