Text Analytics in Marketing by Dan, Daniel; Reutterer, Thomas;

Long description:

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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.

Table of Contents:

Introduction to Text Analytics in Marketing: A Practical Guide for Students and Researchers.- Textual Data, String Handling, Regular Expressions, and Data Structures.- Obtaining Textual Data for Marketing Analytics, Web Scraping, APIs, and Structured Sources.- Basic Text Analysis, Preprocessing, Bag of Words, TF-IDF, and Exploratory Statistics.- Clustering Text for Marketing Segmentation, Similarity Measures and Grouping.- Text Classification, LDA, KNN, SVM, Neural Networks, and Fast Text.- Topic Modeling for Marketing Insights, Latent Dirichlet Allocation and Structural Topic Models.- Sentiment and Emotion Analysis in Marketing, Lexicons, Machine Learning, and Aspect Based Methods.- Named Entity Recognition and Extractive Summarization.- Word Embeddings and Transformers for Marketing Text Analytics.