Analyzing Online Reviews with AI: A No-Code Guide from Raw Data to Research Findings

By Jeong-Yeol Park

Online reviews have become one of the most important data sources in modern research and business strategy. Every day, millions of consumers share their experiences through ratings and written text, creating a rich, constantly updating dataset that captures human satisfaction, preferences, and decision-making patterns across virtually every industry.

This book is designed for graduate students, researchers, and professionals who want to conduct rigorous quantitative analysis of online review data. The analytical methods in this book are implemented through Google Colab and Gemini, an AI assistant that handles the computational work while you focus on what matters most: understanding why each method exists, interpreting the results correctly, and applying the findings appropriately.

The book covers seven core analytical approaches: text-to-numbers conversion through sentiment analysis, topic modeling, and feature engineering; panel regression for controlling entity-level confounds in longitudinal data; causal inference designs, including difference-in-differences, instrumental variables, and selection correction, that move beyond correlation to defensible causal claims; time-series analysis for understanding temporal dynamics and forecasting; spatial econometrics for detecting geographic patterns and diagnosing whether clustering is a real neighborhood effect or a reflection of shared local conditions; structural equation modeling for testing theoretical mechanisms; and machine learning for building predictive models. A closing chapter surveys where review analytics is heading, from large language models to real-time monitoring, multimodal and multilingual analysis, and governance. Along the way, integrated case studies walk through complete end-to-end research projects from question formulation to publication.

Each methods chapter follows a consistent pedagogical approach: WHY the method exists, how to INTERPRET the results, and how to APPLY the findings to real research questions and business decisions.
The examples throughout this book are drawn from multiple domains, including hospitality and restaurants, healthcare, education, finance, technology, and consumer products. While the methods originated in different fields, they share a common thread: they all leverage the power of consumer-generated text and ratings to answer questions that matter.

• Format: Paperback
• Language: English
• ISBN: B0HBM4B7T5
• ISBN13: 9798189036880
• Release Date: July 2026
• Publisher: Independently Published
• Length: 314 Pages
• Weight: 1.21 lbs.
• Dimensions: 0.7" x 7.0" x 10.0"