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Practical Text Analytics - Info and Reading Options

Maximizing the Value of Text Data

Book's cover
The cover of “Practical Text Analytics” - Open Library.
Practical Text Analytics - cover - The Open Library
Book's cover - The Open Library
Practical Text Analytics - cover - Google Books
Book's cover - Google Books

"Practical Text Analytics" is published by Springer in Oct 20, 2018 - Cham, the book is classified in Business & Economics genre, it has 285 pages and the language of the book is English.


“Practical Text Analytics” Metadata:

  • Title: Practical Text Analytics
  • Authors:
  • Language: English
  • Number of Pages: 285
  • Is Family Friendly: Yes - No Mature Content
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham
  • Genres: Business & Economics

“Practical Text Analytics” Subjects and Themes:

Edition Specifications:

  • Format: hardcover

Edition Identifiers:

AI-generated Review of “Practical Text Analytics”:


Snippets and Summary:

This book introduces text analytics as a valuable method for deriving insights from text data.

"Practical Text Analytics" Description:

Google Books:

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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