Peer evaluation has been well established as an effective method to motivate team members to reflect their contribution and performance, to enforce sense of responsibility, to act as an incentive for demonstrating good interpersonal skills and to help the team achieve its goals. Behaviorally anchored rating scales are generally considered an efficient and fair method to measure certain scores. However, in the application of peer evaluation, the results could be influenced by raters’ biased understanding of the scale based on their cultural background. Supplementing peer ratings with information from peer-to-peer comments could provide a way to moderate rater biases. In this paper, we propose a natural language processing model that (1) processes the peer-to-peer comments about rater’s teammates’ teamwork behaviors; (2) converts comments into numerical ratings and compare them to results from peer ratings to validate our proposed system.