Volume 12, Number 1

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election using a Voting Ensemble Approach

  Authors

Ikechukwu Onyenwe¹, Samuel N.C. Nwagbo¹, Ebele Onyedinma¹, Onyedika Ikechukwu-Onyenwe¹, Chidinma A. Nwafor² and Obinna Agbata¹, ¹Nnamdi Azikiwe University, Nigeria, ²Nigerian Army College of Environmental Science and Technology, Nigeria

  Abstract

Nigeria president Buhari defeated his closest rival Atiku Abubakar by over 3 million votes. He was issued a Certificate of Return and was sworn in on 29 May 2019. However, there were claims of widespread hoax by the opposition. The sentiment analysis captures the opinions of the masses over social media for global events. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis through the application of a voting ensemble approach (VEA) in which the predictions from multiple techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment analysis experiment is focused on location-based viewpoints where we used Twitter location data. For this experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of 583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards Buhari and Atiku and their political parties. The overall objective of the use of states/geopolitical zones is to evaluate the similarity between the sentiment of location-based tweets compared to actual election results. The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on Twitter social media in most cases as shown by the polarity scores of different locations, there are also some election results where our location analysis similarity test failed.

  Keywords

Nigeria, Election, Sentiment Analysis, Politics, Tweets, Exploration Data Analysis, location data.