The existing and most commonly used information systems and consumer studies on the functional aspect of 'reputation systems have two streams: the 'rating systems and the 'review content systems'. Both systems are offered by most of the popular e-commerce retailers to enhance customer communication experiences. However, there is limited research on the relationship between customers' rating scores and the sentiments expressed in their review contents, which significantly affects the reliability of the overall review in the reputation systems. A computational linguistics approach (Semantic orientation approach) using Amazon's (UK) product review data (34,621 reviews) is employed to unveil the gap between the numerical ratings and the sentiments of review contents from the two reputation systems. Results show that although customers give the same high rating for products that have a lower/higher overall rating, the customers' expressions of sentiment in the review content are actually less/more positive compared to products that have a higher/lower overall rating.