eXplainable Artificial Intelligence in sentiment analysis of posts about Covid-19 vaccination on Twitter

  • Juliana Da Costa Feitosa UNESP
  • Luiz Felipe De Camargo UNESP
  • Eloisa Bonatti UNESP
  • Giovanna Simioni UNESP
  • José Remo Ferreira Brega UNESP


Considering the impact of the use of Artificial Intelligence (AI) in the most diverse branches of society and the use of eXplicable Artificial Intelligence (XAI) to improve the interpretability of these intelligent models, this paper aims to analyze some existing XAI methods to verify their effectiveness. To this end, experiments were conducted with LIME, SHAP, and Eli5 solutions in a scenario of sentiment classifications in Twitter posts about the Covid-19 vaccination process in Brazil. Thus, it is observed that the tools provide relevant information about the aspects that interfere in the classification of tweets as favorable or not favorable to vaccination, which allows concluding that the methods bring the necessary transparency to confirm the AI decisions regarding the sentiments related to the vaccination process in Brazil.

Palavras-chave: explicable artificial intelligence, explainability, sentiment analysis, COVID-19


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FEITOSA, Juliana Da Costa; DE CAMARGO, Luiz Felipe; BONATTI, Eloisa; SIMIONI, Giovanna; BREGA, José Remo Ferreira. eXplainable Artificial Intelligence in sentiment analysis of posts about Covid-19 vaccination on Twitter. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 65–72.