Sentiment Analysis on Twitter About COVID-19 Vaccination in Mexico

Abstract

This paper conducts a sentiment analysis of Twitter’s posts, between late October 2020 and late April 2021, regarding COVID-19 vaccination campaign in Mexico through several machine learning models such as Logistic Regression, Neuronal Network, Naive Bayes and Support Vector Machine. To prepare data, Natural Language Processing techniques were used such as tokenization, stemming, n-grams and stopwords. The best performance was achieved by Logistic Regression with an accuracy score of 83.42% while classifying tweets according to a positive or negative sense. This work suggests that sentiment analysis with Twitter information allows to witness a relevant part of the public discussion around specific topics. For this study, the tweets analyzed showed a similar behavior to other search and reference electronic tools, such as Google Trends regarding conversation around COVID. In addition, the present analysis allows the classification and tendency of public opinion. Furthermore, this study shows that measuring people’s opinion through machine learning and natural language processing techniques can generate significant benefits for institutions and businesses given that obtaining information on Twitter is less expensive and can be processed and analyzed faster than other opinion analysis techniques such as surveys or focus groups.