How do Fine-Grained Emotions Affect the Helpfulness Vote of a Product Review? Evidence from User Generated Content Using Latent Semantic Analysis

Abstract

With the growth of the internet usage, customers often search online for product information and have access to dozens or hundreds of product reviews from other customers. Some reviews are helpful to the information seeker as evidenced by the helpfulness vote of the review in a website like Amazon, while some other reviews do not get as many helpfulness votes. Recent research showed that helpfulness votes of customer reviews can have a positive influence on sales. While it is clear that the helpfulness vote of a review is important, less is known about why certain pieces of online reviews are more helpful than the others. Past research showed that valence of a review affects the informational value of the content and thus the helpfulness vote of reviews. However, in a purchase or information search situation, people encounter a variety of emotions which are likely to be expressed in the reviews. Potential customers read the reviews to collect or verify information and to see what the other people think. Despite the fact that reviews contain emotions, few studies have investigated how emotions expressed in the review affect the helpfulness of the review. Do discrete emotions have differential informational value in this case?