Abstract: This paper applies Natural Language Processing (NLP) techniques to analyze 20-months of Twitter data related to the general publics’ discourse about the COVID-19 pandemic and teachers. A total of 68,340 English-language tweets were analyzed using semi-supervised topic modeling, which generated 17 topics of interest that focused on schools opening/closing, online learning, vaccines, teacher appreciation, and masks. Using a Robustly optimized BERT approach (RoBERTa), emotion and sentiment analysis were performed longitudinally across all generated topics. Analysis indicated an overwhelmingly negative sentiment, with a focus on emotions of anger and sadness over the 20-month period. Positive sentiment was associated with teacher appreciation each May and at the start of each school year. Qualitative analyses uncovered that spikes in data about vaccines and masks were associated with teachers receiving first vaccination in March of 2021 and parents/students tweeting about teachers not wearing their masks in the classrooms in August/September of 2022.