Practical Text Analytics
Maximizing the Value of Text Data
Nonfiction, Computers, Application Software, Business Software, Business & Finance, Industries & Professions, Industries
| Author: | Murugan Anandarajan, Chelsey Hill, Thomas Nolan | ISBN: | 9783319956633 |
| Publisher: | Springer International Publishing | Publication: | October 19, 2018 |
| Imprint: | Springer | Language: | English |
| Author: | Murugan Anandarajan, Chelsey Hill, Thomas Nolan |
| ISBN: | 9783319956633 |
| Publisher: | Springer International Publishing |
| Publication: | October 19, 2018 |
| Imprint: | Springer |
| Language: | English |
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.
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.
