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Practical Text Analytics

Maximizing the Value of Text Data

  • Murugan Anandarajan
  • Chelsey Hill
  • Thomas Nolan
Book

Part of the Advances in Analytics and Data Science book series (AADS, volume 2)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
    Pages 1-11
  3. Planning the Text Analytics Project

    1. Front Matter
      Pages 13-13
    2. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 15-25
    3. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 27-41
  4. Text Preparation

    1. Front Matter
      Pages 43-43
    2. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 45-59
    3. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 61-73
  5. Text Analysis Techniques

    1. Front Matter
      Pages 75-75
    2. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 77-91
    3. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 93-115
    4. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 117-130
    5. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 131-149
    6. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 151-164
  6. Communicating the Results

    1. Front Matter
      Pages 165-165
    2. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 167-175
    3. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 177-190
  7. Text Analytics Examples

    1. Front Matter
      Pages 191-191
    2. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 193-220
    3. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 221-242
    4. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 243-261
    5. Murugan Anandarajan, Chelsey Hill, Thomas Nolan
      Pages 263-282
  8. Back Matter
    Pages 283-285

About this book

Introduction

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.

Keywords

Text parsing Parsing Text analytics methodology Topic extraction Text visualization Text analytics software Text analytics algorithms Text classification Classification models Tag clouds Sentiment tracking Content analysis perspectives Text mining Automated Content Analysis Theme Extraction Corpus Generation Singular Value Decomposition Unstructured Data Analysis

Authors and affiliations

  • Murugan Anandarajan
    • 1
  • Chelsey Hill
    • 2
  • Thomas Nolan
    • 3
  1. 1.LeBow College of BusinessDrexel UniversityPhiladelphiaUSA
  2. 2.Feliciano School of BusinessMontclair State UniversityMontclairUSA
  3. 3.Mercury Data ScienceHoustonUSA

Bibliographic information