Presidential preferences in Colombia through Sentiment Analysis

Abstract

This work carries out the sentiment analysis of the social network Twitter regarding the presidential debate on May 23, where a hashtag was left open so viewers could give their points of view on these three candidates: Gustavo Petro, Federico Gutierrez, and Rodolfo Hernández. Once we extracted these Tweets contained in the hashtag, they were manually classified. They then went through all the pre-processing and elimination of special characters, links, URLs, images, or videos. Next, the TextVectorization layer from the TensorFlow library was used to convert these tweets to vectors and finally to go through the two models. The results show the best results for the BERT model with an accuracy of 76% and an F1 score of 85%.