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024 7 $a10.1007/978-1-4842-2388-8
035   $a(CKB)3710000000965188
035   $a(DE-He213)978-1-4842-2388-8
035   $a(MiAaPQ)EBC4751439
035   $a(CaSebORM)9781484223871
035   $a(PPN)197142117
035   $a(OCoLC)980303853
035   $a(OCoLC)ocn980303853
035   $a(EXLCZ)993710000000965188
100   $a20161130d2016      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aText Analytics with Python $eA Practical Real-World Approach to Gaining Actionable Insights from your Data /$fby Dipanjan Sarkar
205   $a1st ed. 2016.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2016.
215   $a1 online resource (XXI, 385 p. 54 illus., 33 illus. in color.) 
300   $aIncludes index.
311 08$a9781484223871 
311 08$a148422387X 
311 08$a9781484223888 
311 08$a1484223888 
327   $aChapter 1:Natural Language Basics -- Chapter 2:Python Refresher for Text Analytics -- Chapter 3:Text Processing -- Chapter 4:Text Classification -- Chapter 5:Text summarization and topic modeling -- Chapter 6:Text Clustering and Similarity analysis -- Chapter 7:Sentiment Analysis.-.
330   $aDerive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern.
606   $aBig data
606   $aDatabase management
606   $aData mining
606   $aProgramming languages (Electronic computers)
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
615  0$aBig data.
615  0$aDatabase management.
615  0$aData mining.
615  0$aProgramming languages (Electronic computers)
615 14$aBig Data.
615 24$aDatabase Management.
615 24$aData Mining and Knowledge Discovery.
615 24$aProgramming Languages, Compilers, Interpreters.
676   $a004
700   $aSarkar$b Dipanjan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0785722
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910154842603321
996   $aText analytics with Python$91749316
997   $aUNINA