Consumers are frequently sharing digitalized opinions such as online reviews to aid purchase decision of others. While each online review is supposed to portray an individual’s unique and genuine experience, previous consumers’ reviews may to some extent influence others’ subsequent reviews when the next consumers perceive the previous ones to be misleading. This study aims to examine the causal impact of previous average rating on provision of emotional text in a subsequent online review, especially when displayed rating is off from its true rating. Using online review data collected from Yelp, we apply natural language processing to measure the amount of emotions in each review and, then, use a regression discontinuity design by taking advantage of the Yelp’s rounded rating display. Our preliminary results suggest that, when the displayed rating is different from the true rating, subsequent reviews tend to include more emotional text. Specifically, a higher displayed rating leads to a sharp increase in provision of negative emotions such as sadness, fear, disgust, and anger.