Modeling the impact of review dynamics on utility value of a product

Abstract

Manufacturer-provided specifications often do not provide a true picture of the utility value of a product. A product’s true assessed value is the result of consumer opinion often conveyed via word of mouth. The increasing popularity of social media has led to the inevitable integration of the social platform with e-commerce sites where consumers share their opinions on products and prospective buyers seek the opinion of their peers before making a purchase. The influencing power of these social platforms has led to researchers mining these opinions and utilizing them to assess the value of the product. Consumer opinion can vary greatly and is dependent on several factors such as when the product is launched into the market, what competitors are offering and how their product is faring over time, etc. Hence, the assessed value of a product is subject to significant dynamism which if modeled accurately, can provide several business insights. Experience has taught us that accurately capturing the time at which opinions are expressed and identifying the attributes that influence these opinions play an important role in determining assessed value; our model aims to capture this information accordingly. Our experiments are based on large-scale review sets (approximately 30,000 reviews) collected from real-world portals such as Amazon, Mouthshut and IMDB. Validation using this real-world data confirms the superiority of our model. We demonstrate that the utility value when modeled as a function of time on the most valued attributes, provides business insights.