LEADER 03957nam 2200481 450 001 9910157529003321 005 20231215095816.0 010 $a1-78217-470-2 035 $a(CKB)3710000001001282 035 $a(MiAaPQ)EBC4774286 035 $a(CaSebORM)9781783551811 035 $a(PPN)220203008 035 $a(EXLCZ)993710000001001282 100 $a20170301h20162016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMastering text mining with R $emaster text-taming techniques and build effective text-processing applications with R /$fAshish Kumar, Avinash Paul 205 $aFirst edition 210 1$aBirmingham, England ;$aMumbai, [India] :$cPackt,$d2016. 210 4$dİ2016 215 $a1 online resource (259 pages) 300 $aIncludes index. 311 $a1-78355-181-X 330 $aMaster text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If 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 Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover 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 sentence completing application Perform entity extraction and named entity recognition using R In Detail 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 m... 606 $aText processing (Computer science) 606 $aR (Computer program language) 606 $aData mining 615 0$aText processing (Computer science) 615 0$aR (Computer program language) 615 0$aData mining. 676 $a005 700 $aKumar$b Ashish$c(Data scientist),$01233813 702 $aPaul$b Avinash 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910157529003321 996 $aMastering text mining with R$92865748 997 $aUNINA