Measuring the Impact of Language Models in Sentiment Analysis for Mexico's COVID-19 Pandemic

The world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population's mental health and health-related programs. It is complex for public organizations to measure the population's mental health to incorporate its feedback into their decision-making process. A significant portion of the population has turned to social media to express the details of their daily life, making these public data a rich field for understanding emotional and mental well-being. To this end, by using open sentiment analysis tools, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies. Several modern language models were evaluated and compared using intrinsic and extrinsic tasks, that is, the sentiment analysis evaluation of public domain tweets related to the COVID-19 pandemic in Mexico. This study provides a fair evaluation of state-of-the-art language models, such as BERT and VADER, showcasing their metrics and comparing their performance against a real-world task. Results show the importance of selecting the correct language model for large projects such as this one, for there is a need to balance costs with the model's performance.