Harnessing Social Media Sentiment for Predictive Insights into the Nigerian Presidential Election

Conference paper

Alao, J. O., Amirhosseini, M., Karami, A. and Ghorashi, S. A. 2025. Harnessing Social Media Sentiment for Predictive Insights into the Nigerian Presidential Election. Cognitive Models and Artificial Intelligence Conference. Prague-Czech Republic 13 - 14 Jun 2025 IEEE.
AuthorsAlao, J. O., Amirhosseini, M., Karami, A. and Ghorashi, S. A.
TypeConference paper
Abstract

Political events are heavily influenced by social media, shaping public opinion and actions. Sentiment analysis of social media content helps policymakers, campaign planners, and analysts understand voter sentiments for informed decision-making. This study performs a comprehensive comparative analysis of traditional machine learning models— Logistic Regression, Random Forest, Naïve Bayes, and SVM—and deep learning models—FFNN, LSTM, and CNN—on tweets collected via the X (formerly Twitter) API regarding the 2023 Nigerian Presidential Election. All models underwent a proper optimisation process and were evaluated using key performance evaluation metrics. Over 1.9 million tweets were collected over eight months. Results show deep learning models outperform traditional ones, with LSTM achieving the highest accuracy (95%), followed by CNN (94%) and FFNN (94%).

KeywordsSentiment Analysis; Deep Learning; Machine Learning; Social Media Analysis; Election
Year2025
ConferenceCognitive Models and Artificial Intelligence Conference
PublisherIEEE
Accepted author manuscript
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Anyone
Publication process dates
Accepted03 May 2025
Deposited14 May 2025
Journal citationp. In press
ISBN979-8-3315-0969-9
Copyright holder© 2025 IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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