Sentiment Analysis of Twitter Posts About the 2017 Academy Awards

Igor T. Correa; Daniel D. Abdala; Rodrigo S. Miani; Elaine R. Faria

doi:10.5753/eniac.2018.4427

Igor T. Correa UFU
Daniel D. Abdala UFU
Rodrigo S. Miani UFU
Elaine R. Faria UFU

DOI: https://doi.org/10.5753/eniac.2018.4427

Resumo

This paper aims to perform the sentiment analysis of Twitter posts related to the movies nominated for Best Picture of the 2017 Oscars in order to find out if there is a correlation between the posts and the Oscar winners. A tweets database was built, pre-processed, and later evaluated by three distinct approaches: Naive Bayes, Distant Supervision Learning, and Polarity Function. It was possible to predict which movie would be considered the winner and which would be among the less prestigious ones. It was noted that Twitter users prefer to post positive comments about movies rather than saying bad things about the ones they did not like. Furthermore, it was verified that award shows such as the Oscars cause a growth in the number of posts on Twitter.

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