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<p class="MsoNormal"><span lang="EN-US" style="font-size: 11.0pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US;">This book offers a comprehensive introduction to text mining and text analytics tailored for marketers. It presents key techniques for analyzing, compressing, classifying, and visualizing textual data and user-generated content (UGC), with a particular emphasis on using R software. These methods enable readers to effectively prepare and manipulate textual data to uncover actionable marketing insights.</span></p><p class="MsoNormal"><span lang="EN-US" style="font-size: 11.0pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US;">In today’s digital landscape, analyzing online chatter, sentiment, preferences, and other forms of electronic word-of-mouth has become an essential skill for marketing researchers and professionals. Through a rich collection of examples, program code, and hands-on exercises, this book equips both students and marketing managers with the theoretical foundation and practical skills needed to apply text-based data analysis to contemporary marketing challenges.</span></p>

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Text Analytics in Marketing

  • 教材

  • 发表日期：

    2026年

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  • Daniel Dan,
  • Thomas Reutterer

摘要

This book offers a comprehensive introduction to text mining and text analytics tailored for marketers. It presents key techniques for analyzing, compressing, classifying, and visualizing textual data and user-generated content (UGC), with a particular emphasis on using R software. These methods enable readers to effectively prepare and manipulate textual data to uncover actionable marketing insights.

In today’s digital landscape, analyzing online chatter, sentiment, preferences, and other forms of electronic word-of-mouth has become an essential skill for marketing researchers and professionals. Through a rich collection of examples, program code, and hands-on exercises, this book equips both students and marketing managers with the theoretical foundation and practical skills needed to apply text-based data analysis to contemporary marketing challenges.