This is the video course, for people who will run the methods themselves. Fourteen narrated lessons on the published book. You run each method on a practice dataset, then write the finding twice: once as a results paragraph, once as a Monday decision. Lesson one and the mini-project are free and need no account. Faculty assigning the book as a class text should request the instructor decks from the Academy page. That path is not this one.
This is the first end-to-end analysis in the book, and it comes before any advanced method. In about thirty minutes you load sample review data, summarize it, find what positive and negative reviewers talk about, measure sentiment, and turn the results into recommendations. You do not need to write code. You type plain requests to Gemini and read the output.
Seven steps in all. The first three are below in full, exactly as the book gives them. Open Google Colab with Gemini enabled, download the mini-project file below, upload it to Colab, and type. The prompts name that exact file, so nothing needs adjusting; if you use your own data instead, just swap the filename in Step 1.
What you should see: a table of the first 5 rows, a list of column names, and a single number showing how many reviews are in the file.
What you should see: a table of rating counts, a bar chart showing the distribution visually, and a short paragraph in everyday language describing what the pattern means.
What you should see: a ranked list of 15 words with their counts, showing the most common terms in low-rated reviews.
Steps 4 through 7 continue the same way: the word list for four-star and five-star reviews, a sentiment score computed for every review, sentiment compared against star rating, and three concrete recommendations a manager could act on. That last step is the whole program in miniature, a number you produced yourself turned into something someone can do. The book carries all seven with an answer key, a teaching note on why sentiment and stars do not line up perfectly, and a fix for each step that can misbehave.
Maple City is the book's companion dataset: a fictional mid-sized city whose restaurant reviews carry the whole methods sequence, from text measurement through panel regression, forecasting, spatial diagnostics, and prediction. It is fully synthetic and built with known ground truth, so any result can be checked against the effect it was meant to recover. It is free to copy and reuse.
Download the mini-project fileThe full Maple City practice dataset, the one the book’s worked examples run on, is here too: maple_city_reviews.csv.
Run the method on the practice dataset. Read the output correctly, which is the part most courses skip. Then apply it twice: once as a researcher, where the finding becomes a results paragraph, and once as an analyst or operator, where it becomes a decision. The book is built this way. Chapter 6 does not stop at the regression table; it writes the same finding out for a journal and for hospital leadership, one after the other. Chapter 12 asks you to restate a case finding as a two-sentence executive summary a non-technical manager could act on.
Here is one fixed-effects result from Chapter 6, read both ways. Same model, same numbers, two registers.
"Fixed-effects panel regression on 1,847 hospital-quarter observations reveals that average review sentiment is a significant positive predictor of hospital star ratings (β = 0.342, SE = 0.089, p < 0.001), controlling for review volume, management response rate, and topic composition."
"A meaningful improvement in patient sentiment is associated with roughly a third of a star increase in ratings, controlling for other factors. We identified the specific complaint themes that track with lower ratings: wait times, staff interactions, and cleanliness."
No jargon in the second one, no p-values, concrete themes, and a place to start. Producing both from one result you ran yourself is what the program trains, and it trains it on every method, not only regression.
This is the self-serve level of the discipline, taught honestly as that. It does not hand anyone a production analytics system, and the book is candid about the limits: when the data are thin, a careful descriptive analysis paired with one well-chosen method is the honest move, not the full method stack.
Analyzing Online Reviews with AI: A No-Code Guide from Raw Data to Research Findings, by Jeong-Yeol Park. Its claim is that reviews are multi-layer behavioral data that need an integrated analytical system: text features feed panel regressions, temporal patterns inform causal identification, geographic structure reveals spillovers, and latent mechanisms explain why the patterns exist. Each method chapter names what it adds that the others cannot.
Available as paperback (ISBN 979-8-1890-3688-0) and Kindle (ASIN B0HBLZ5D4J).
The curriculum page is the map: what each chapter leaves you able to do, plus three Colab checks you can run today. This page is the course. Faculty decks are requested from the Academy page, not from here.
The full program is $249 at the introductory price, for the first thirty seats, then $399. Both programs together are $399 introductory, $649 after. The increase is announced in advance, here and on the Academy page.