Text Analytics in Marketing: A Practical Guide for Students and Researchers
暫譯: 行銷中的文本分析：學生與研究者的實用指南

Daniel Dan is Assistant Professor and founder of the School of Applied Data Science at Modul University Vienna (MU Vienna), Austria. His expertise focuses on natural language processing, information retrieval, and data-driven marketing analytics in text-rich environments. His primary research, teaching, and collaboration interests include generative AI, sentiment and opinion mining, data visualization, and decision support. He teaches data science and AI courses at MU Vienna and AI at the WU (Vienna University of Economics and Business) Executive Academy. His work features in peer-reviewed journals and international conferences, and he contributes to EU-funded projects on information overload and digital well-being.

Thomas Reutterer is Professor of Marketing at the Vienna University of Economics and Business (WU Vienna), Austria. His expertise focuses on analyzing, modeling and forecasting customer behavior in data-rich environments. His primary research, teaching and business consulting interests are focused in areas of retail and digital services, customer value and relationship management, and marketing models for customer-base analysis and decision support. His prior research has appeared in leading marketing and operations management journals.

丹尼爾·丹（Daniel Dan）是奧地利維也納莫杜大學（Modul University Vienna, MU Vienna）應用數據科學學院的助理教授及創始人。他的專業領域集中在自然語言處理、資訊檢索以及在文本豐富環境中的數據驅動行銷分析。他的主要研究、教學和合作興趣包括生成式人工智慧（generative AI）、情感與意見挖掘、數據視覺化以及決策支持。他在MU Vienna教授數據科學和人工智慧課程，並在維也納經濟與商業大學（WU）高階學院教授人工智慧。他的研究成果發表於同行評審的期刊和國際會議，並參與歐盟資助的有關資訊過載和數位福祉的項目。

托馬斯·魯特勒（Thomas Reutterer）是奧地利維也納經濟與商業大學（WU Vienna）的行銷學教授。他的專業領域集中在分析、建模和預測數據豐富環境中的顧客行為。他的主要研究、教學和商業諮詢興趣集中在零售和數位服務、顧客價值和關係管理，以及用於顧客基礎分析和決策支持的行銷模型。他之前的研究成果已發表於領先的行銷和運營管理期刊。