Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

Abstract

Sentiment analysis is a natural language processing (NLP) tool that uses automatic methods to capture the opinions of the masses over social media such as Twitter, especially for election monitoring and predictions. However, recent researches have explored mining of public sentiment in tweets neglecting using tweets location features and ensemble learning of NLP. 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 using Twitter location features. 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 the major candidates (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.

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

“…Similarly, Onyenwe et al (2022) conducted a study in 2021 focusing on sentiment analysis of tweets related to the 2019 Nigerian presidential election. They employed a voting ensemble approach (VEA) that combined predictions from multiple techniques to determine the most appropriate polarity of each tweet.…”

“…Overall, both studies employed innovative approaches to analyzing election-related data. While the model developed by Awais et al (2019) focused on accurately forecasting election outcomes in Pakistan, Onyenwe et al (2022) concentrated on sentiment analysis of tweets during the Nigerian presidential election. These studies contribute to the growing field of machine learning techniques to gain insights into electoral processes and public opinion.…”