Text Analytics for Business Decisions : : A Case Study Approach / / Andres Fortino.

• VerfasserIn: Fortino, Andres,
• Place / Publishing House: Dulles, VA : : Mercury Learning and Information, , [2021]; ©2021
• Year of Publication: 2021
• Language: English
• Physical Description: 1 online resource (318 p.)
• Other title: Frontmatter --; Contents --; Preface --; Chapter 1 Framing Analytical Questions --; Chapter 2 Analytical Tool Sets --; Chapter 3 Text Data Sources and Formats --; Chapter 4 Preparing the Data File --; Chapter 5 Word Frequency Analysis --; Chapter 6 Keyword Analysis --; Chapter 7 Sentiment Analysis --; Chapter 8 Visualizing Text Data --; Chapter 9 Coding Text Data --; Chapter 10 Named Entity Recognition --; Chapter 11 Topic Recognition in Documents --; Chapter 12 Text Similarity Scoring --; Chapter 13 Analysis of Large Datasets by Sampling --; Chapter 14 Installing R and RStudio --; Chapter 15 Installing the Entity Extraction Tool --; Chapter 16 Installing the Topic Modeling Tool --; Chapter 17 Installing the Voyant Text Analysis Tool --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781683926658
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Andres Fortino.

With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today’s most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises. FEATURES: Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented laterUses Excel and R for datasets in case studies and exercisesFeatures the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data miningCompanion files with numerous datasets and figures from the text.The companion files are available online by emailing the publisher with proof of purchase at [e-mail].