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Mastering Text Mining with R
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Index
Mastertext-tamingtechniquesandbuildeffectivetext-processingapplicationswithRAboutThisBook?Developalltherelevantskillsforbuildingtext-miningappswithRwiththiseasy-to-followguide?Gainin-depthunderstandingofthetextminingprocesswithlucidimplementationintheRlanguage?Example-richguidethatletsyougainhigh-qualityinformationfromtextdataWhoThisBookIsForIfyouareanRprogrammer,analyst,ordatascientistwhowantstogainexperienceinperformingtextdataminingandanalyticswithR,thenthisbookisforyou.Exposuretoworkingwithstatisticalmethodsandlanguageprocessingwouldbehelpful.WhatYouWillLearn?GetacquaintedwithsomeofthehighlyefficientRpackagessuchasOpenNLPandRWekatoperformvariousstepsinthetextminingprocess?AccessandmanipulatedatafromdifferentsourcessuchasJSONandHTTP?Processtextusingregularexpressions?Gettoknowthedifferentapproachesoftaggingtexts,suchasPOStagging,togetstartedwithtextanalysis?Exploredifferentdimensionalityreductiontechniques,suchasPrincipalComponentAnalysis(PCA),andunderstanditsimplementationinR?Discovertheunderlyingthemesortopicsthatarepresentinanunstructuredcollectionofdocuments,usingcommontopicmodelssuchasLatentDirichletAllocation(LDA)?Buildabaselinesentencecompletingapplication?PerformentityextractionandnamedentityrecognitionusingRInDetailTextMining(ortextdataminingortextanalytics)istheprocessofextractingusefulandhigh-qualityinformationfromtextbydevisingpatternsandtrends.Rprovidesanextensiveecosystemtominetextthroughitsmanyframeworksandpackages.Startingwithbasicinformationaboutthestatisticsconceptsusedintextmining,thisbookwillteachyouhowtoaccess,cleanse,andprocesstextusingtheRlanguageandwillequipyouwiththetoolsandtheassociatedknowledgeaboutdifferenttagging,chunking,andentailmentapproachesandtheirusageinnaturallanguageprocessing.Movingon,thisbookwillteachyoudifferentdimensionalityreductiontechniquesandtheirimplementationinR.Next,wewillcoverpatternrecognitionintextdatautilizingclassificationmechanisms,performentityrecognition,anddevelopanontologylearningframework.Bytheendofthebook,youwilldevelopapracticalapplicationfromtheconceptslearned,andwillunderstandhowtextminingcanbeleveragedtoanalyzethemassivelyavailabledataonsocialmedia.StyleandapproachThisbooktakesahands-on,example-drivenapproachtothetextminingprocesswithlucidimplementationinR.
目錄(60章)
倒序
- coverpage
- Mastering Text Mining with R
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- eBooks discount offers and more
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Statistical Linguistics with R
- Probability theory and basic statistics
- Language models
- Quantitative methods in linguistics
- R packages for text mining
- Summary
- Chapter 2. Processing Text
- Accessing text from diverse sources
- Processing text using regular expressions
- Normalizing texts
- Lexical diversity
- Language detection
- Summary
- Chapter 3. Categorizing and Tagging Text
- Parts of speech tagging
- Hidden Markov Models for POS tagging
- Collocation and contingency tables
- Feature extraction
- Summary
- Chapter 4. Dimensionality Reduction
- The curse of dimensionality
- Dimensionality reduction
- Correspondence analysis
- Summary
- Chapter 5. Text Summarization and Clustering
- Topic modeling
- Latent semantic analysis
- Text clustering
- Document clustering
- Sentence completion
- Summary
- Chapter 6. Text Classification
- Text classification
- Document representation
- Kernel methods
- Bias–variance trade-off and learning curve
- Learning curve
- Dealing with reducible error components
- Summary
- Chapter 7. Entity Recognition
- Entity extraction
- Sentence boundary detection
- Named entity recognition
- Summary
- Index 更新時間:2021-04-02 20:39:06
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