Jessica Salinas, Carlos Flores, Hector Ceballos and Francisco Cantu, Tecnologico de Monterrey, Mexico
The amount of information that social networks can shed on a certain topic is exponential compared to conventional methods. As new COVID-19 vaccines are approved by COFEPRIS in Mexico, society is acting differently by showing approval or rejection of some of these vaccines on social networks. Data analytics has opened the possibility to process, explore, and analyze a large amount of information that comes from social networks and evaluate people's sentiments towards a specific topic. In this analysis, we present a Sentiment Analysis of tweets related to COVID-19 vaccines in Mexico. The study involves the exploration of Twitter data to evaluate if there are preferences between the different vaccines available in Mexico and what patterns and behaviors can be observed in the community based on their reactions and opinions. This research will help to provide a first understanding of people's opinions about the available vaccines and how these opinions are built to identify and avoid possible misinformation sources.
Twitter, Data Mining, Sentiment Analysis, Machine Learning, COVID-19.