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Practical text analytics = maximizin...

Anandarajan, Murugan.

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Practical text analytics = maximizing the value of text data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical text analytics/ by Murugan Anandarajan, Chelsey Hill, Thomas Nolan.
其他題名:	maximizing the value of text data /
作者:	Anandarajan, Murugan.
其他作者:	Hill, Chelsey.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xxviii, 285 p. :ill., digital ;24 cm.
內容註:	Chapter 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.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-319-95663-3
ISBN:	9783319956633

Practical text analytics = maximizing the value of text data /
Anandarajan, Murugan.

Practical text analyticsmaximizing the value of text data /[electronic resource] :by Murugan Anandarajan, Chelsey Hill, Thomas Nolan. - Cham :Springer International Publishing :2019. - xxviii, 285 p. :ill., digital ;24 cm. - Advances in analytics and data science,v.22522-0233 ;. - Advances in analytics and data science ;v.2..

Chapter 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.

This 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.

ISBN: 9783319956633

Standard No.: 10.1007/978-3-319-95663-3doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Practical text analytics = maximizing the value of text data /
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505 0 $a Chapter 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 $a This 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.
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