Sentiment Analysis is a field of Natural Language Processing which aims at classifying the author's sentiment in text. This paper first describes a sentiment analysis model for students' comments about professor performance. The model achieved impressive results for comments collected from student surveys conducted at a private university in 2019/20. Then, it applies the model to different scenarios: (1) in-person classes taught in 2019 (pre-COVID); (2) the emergency shift to online, synchronous classes taught in the first semester of 2020 (early-COVID); and (3) the planned online classes taught in the second semester of 2020 (late-COVID). The results show that students acknowledged the effort professors did to keep classes running during the first semester of 2020, and that the enthusiasm continued throughout the second semester. Furthermore, the results show that students evaluated professors' performance for online courses better than for in-person courses. [For the full proceedings, see ED615472.]