Evaluation of the Informational Weight of Online Reviews via TF-IDF: A Case Study of Amazon Product Reviews
Jie Ji · Advances in Economics Management and Political Sciences · 2025
E-commerce has become a vital part of people’s lives. Moreover, Online customer reviews have become a crucial reference for customers’ decision-making process. A large number of studies have emphasized the role of review helpfulness and sentiment. However, little attention has been given to the actual relationship between review length and its informative value. This study primarily examines whether review length can be a reasonable indicator of the review’s informativeness. This study uses the Amazon Product Review dataset from Kaggle, which contains more than 568000 reviews from different categories. Using Python as the primary tool, this study conducts quantitative data analysis, data modeling, and visualization skills to explore the correlation between the two variables. The Term Frequency-Inverse Document Frequency (TF-IDF) represents the lexical richness of the review, indicating informativeness in this study. The results reveal a statistically significant positive correlation, with diminishing returns as length increases. The findings suggest that while longer reviews are generally more informative, length alone cannot fully determine review quality. This study provides exploratory insights for consumers and merchants regarding the interpretation and evaluation of online reviews.