Practical Text Analytics: Maximizing the Value of Text Data

Practical Text Analytics: Maximizing the Value of Text Data
Practical Text Analytics: Maximizing the Value of Text Data (Advances in Analytics and Data Science)
By 作者: Murugan Anandarajan – Chelsey Hill – Thomas Nolan
ISBN-10 书号: 3319956620
ISBN-13 书号: 9783319956626
Edition 版本: 1st ed. 2019
Release Finelybook 出版日期: 2018-10-20
pages 页数: (285 )


Book Description to Finelybook sorting

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.
Front Matter
1. Introduction to Text Analytics
Part l. Planning the Text Analytics Project
2. The Fundamentals of Content Analysis
3. Planning for Text Analytics
Part ll. Text Preparation
4. Text Preprocessing
5. Term-Document Representation
Part ll. Text Analysis Techniques
6. Semantic Space Representation and Latent Semantic Analysis
7. Cluster Analysis: Modeling Groups in Text
8. Probabilistic Topic Models
9. Classification Analysis: Machine Learning Applied to Text
10. Modeling Text Sentiment: Learning and Lexicon Models
Part IV. Communicating the Results
11. Storyteling Using Text Data
12. Visualizing Analysis Results
Part V. Text Analytics Examples
13. Sentiment Analysis of Movie Reviews UsingR
14. Latent Semantic Analysis (LSA) in Python
15. Learning-Based Sentiment Analysis Using RapidMiner
16. SAS Visual Text Analytics

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