The Tip of the Buyer: Extracting Product Tips from Reviews

Product reviews play a key role in e-commerce platforms. Studies show that many users read product reviews before a purchase and trust them to the same extent as personal recommendations. However, in many cases, the number of reviews per product is large and extracting useful information becomes a challenging task. Severalwebsites have recently added an option to post tips-short, concise, practical, and self-contained pieces of advice about the products. These tips are complementary to the reviews and usually add a new non-trivial insight about the product, beyond its title, attributes, and description. Yet, most if not all major e-commerce platforms lack the notion of a tip as a first-class citizen and customers typically express their advice through other means, such as reviews. In this work, we propose an extractive method for tip generation from product reviews. We focus on five popular e-commerce domains whose reviews tend to contain useful non-trivial tips that are beneficial for potential customers. We formally define the task of tip extraction in e-commerce by providing the list of tip types, tip timing (before and/or after the purchase), and connection to the surrounding context sentences. To extract the tips, we propose a supervised approach and leverage a publicly available dataset, annotated by human editors, containing 14,000 product reviews. To demonstrate the potential of our approach, we compare different tip generation methods and evaluate them both manually and over the labeled set. Our approach demonstrates particularly high performance for popular products in the Baby, Home Improvement, and Sports & Outdoors domains, with precision of over 95% for the top 3 tips per product. In addition, we evaluate the performance of our methods on previously unseen domains. Finally, we discuss the practical usage of our approach in real-world applications. Concretely, we explain how tips generated from user reviews can be integrated in various use cases within e-commerce platforms and benefit both buyers and sellers.

Automated summary

Product reviews are crucial for e-commerce platforms, as users heavily rely on them before making purchases. However, extracting useful information from a large number of reviews can be challenging. To address this, some websites have introduced the option of posting tips, which provide practical insights about products. In this study, we propose a method to extract tips from product reviews in popular e-commerce domains. We use a supervised approach and evaluate different methods, demonstrating high performance in certain domains. We also discuss the practical applications of our approach in real-world scenarios, benefiting both buyers and sellers.