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

• URL: http://arxiv.org/abs/2112.04351v3
• Date: Mon, 18 Sep 2023 08:10:41 GMT
• Title: Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data
• Authors: Tian Yan, Fang Liu
• Abstract summary: We investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period. We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment.
• Score: 3.5966786737142304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media. Method: We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment. Results: The results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a $p$-value $<0.001$; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a $p$-value of 0.029. Conclusions: Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.

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