Oscillation of tweet sentiments in the election of João Doria Jr. for Mayor

Abstract

The purpose of this work is to identify and analyze the oscillation of sentiments expressed by users of the Twitter social media through their direct replies to posts by user @jdoriajr that took place before, during and after the elections for mayor of the city of São Paulo in the year 2016. In order to make this research possible, we used Python 3.6.4 and the Searchtweets 1.6.1 library for consumption of the API Search Twitter, from which it was possible to extract 76,690 tweets. Text sentiment analysis was carried out through the Lexicon-Based Approach method and the Laplacian Smoothing calculation algorithm-which generated a rate that would represent a negative and a positive sentiment ranging from − 0.1306 (minimum) to 0.1489 (maximum) respectively, throughout the observed period. As additional tools, WordCloud and t-SNE (t-Distributed Stochastic Neighbor Embedding) Corpus Visualization were used for visualization of the word cloud and cluster, respectively, with both functionalities available at the Yellowbrick 0.8 package also for Python.