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008			181019s2019 gw | o |||| 0|eng d
020			_a3319956639
024	7		_a10.1007/978-3-319-95663-3 _2doi
040			_bspa _dES-VaUE
072		7	_aKJQ _2bicssc
072		7	_aBUS070030 _2bisacsh
072		7	_aKJQ _2thema
082	0		_a005.7 _223
100	1		_aAnandarajan, Murugan., _eauthor _4aut
245	1	0	_aPractical Text Analytics : _bMaximizing the Value of Text Data _cby Murugan Anandarajan, Chelsey Hill, Thomas Nolan
264		1	_aCham _bSpringer International Publishing _bImprint: Springer _c2019
300			_a1 online resource (294 pages)
336			_atext _btxt [ _2rdacontent]
337			_acomputer _bc [ _2rdamedia]
338			_aonline resource _bcr [ _2rdacarrier]
490	1		_aAdvances in Analytics and Data Science, _x2522-0233 ; _v2
505	0		_aChapter 1. Introduction to Text Analytics -- Chapter 2. Fundamentals of Content Analysis -- Chapter 3. Text Analytics Roadmap -- Chapter 4. Text Pre-Processing -- Chapter 5. Term-Document Representation -- Chapter 6. Semantic Space Representation and Latent Semantic Analysis -- Chapter 7. Cluster Analysis: Modeling Groups in Text -- Chapter 8. Probabilistic Topic Models -- Chapter 9. Classification Analysis: Machine Learning Applied to Text -- Chapter 10. Modeling Text Sentiment: Learning and Lexicon Models -- Chapter 11. Storytelling Using Text Data -- Chapter 12. Visualizing Results -- Chapter 13. Sentiment Analysis of Movie Reviews using R -- Chapter 14. Latent Semantic Analysis (LSA) in Python -- Chapter 15. Learning-Based Sentiment Analysis using RapidMiner -- Chapter 16. SAS Visual Text Analytics
520			_aThis book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information
650		0	_aDatos masivos
650		0	_aManagement information systems
650		0	_aStatistics
650	1	4	_aBig Data/Analytics.
650	2	4	_aBusiness Information Systems.
650	2	4	_aStatistics for Business, Management, Economics, Finance, Insurance.
700	1		_aHill, Chelsey., _eauthor _4aut
700	1		_aNolan, Thomas., _eauthor _4aut
776			_z3-319-95662-0
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-95663-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
998			_db