Dear Internet Researchers: My marketing analytics course has exclusively focused on analyzing NUMERICAL marketing-related data. I, however, am now INTRODUCING my undergraduate students to (NON-NUMERICAL) TEXT data and their analysis. I seek your help in critiquing what I do and advising me on what and how I can do better. In the past, I have used any one of the following three data samples: * A brand's (say, Toyota's) Facebook data (brand's posts and users' comments), * Consumers' Tweets on a consumption-related phenomenon, and * News-media reports on a recent controversy (e.g., election results) obtained from Factiva. Here's what I do in the one-hour session: 1. Motivate by listing the four forms (text, voice, image, and video) and sources (e.g., social media, call center, product packages, and video reviews of products respectively for the four forms) of non-numerical data, 2. Introduce the data sample (one of the above three) and tell students how they can obtain (for free and $) similar data themselves, 3. Introduce SENTIMENT and TOPICS (aka themes, ideas) as two commonly measured characteristics of the text, and 4. DEMONSTRATE LIWC for sentiment and MALLET GUI to topic mining/modeling. If you have suggestions on how I can improve my students' learning experience (e.g., critique my approach and/or content, recommend characteristics other than sentiment and topic, recommend readings, suggest more appropriate and relevant text data, recommend intuitive software programs), please consider helping me. Thanks very much! Vivek Astvansh Assistant Professor of Marketing, Kelley School of Business, Indiana University Bloomington https://kelley.iu.edu/faculty-research/faculty-directory/profile.cshtml/?id=...