Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data

The COVID-19 pandemic has affected societies and human health and well-being in various ways. In this study, we collected Reddit data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with 8 universities, applied natural language processing (NLP) techniques, and trained graphical neural networks with social media data, to study how the pandemic has affected people's emotions and psychological states compared to the pre-pandemic era. Specifically, we first applied a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn embedding from the semantic information of Reddit messages and trained a graph attention network (GAT) for sentiment classification. The usage of GAT allows us to leverage the relational information among the messages during training. We then applied subgroup-adaptive model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the manually labeled and model-predicted sentiment labels on the collected data, we applied a generalized linear mixed-effects model to estimate the effects of pandemic and online teaching on people's sentiment in a statistically significant manner. The results suggest the odds of negative sentiments in 2020 is $14.6\%$ higher than the odds in 2019 ($p$-value $<0.001$), and the odds of negative sentiments are $41.6\%$ higher with in-person teaching than with online teaching in 2020 ($p$-value $=0.037$) in the studied population.