Student Evaluation of Teaching surveys (SETs) are used at universities to inform teaching practice and subject design. However, there is increasing concern about the impact of allegations, abuse, and discrimination in survey open text components. Here we discuss the implementation of an automated screening mechanism using a combination of dictionary and machine learning approaches. We present both a process diagram detailing how the screening is performed, as well as a form of categorisation for comments that are unacceptable or indicate a potential risk of harm. Examples of real comments in each of these categories are presented to demonstrate the depth of the challenge and potential cause for concern. Ultimately, we argue that student and educator wellbeing are inextricably connected and exposing staff to abusive and discriminatory comments causes harm. Furthermore, SETs are an important channel for students to raise concerns about their own wellbeing and potentially unsafe experiences in the learning environment.