Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people’s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public’s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.

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Many cutting-edge language models have been used in the past to forecast election results. Sentiment analysis aids in opinion mining – a common experiment used to detect public opinions – on a given topic. Twitter has gained popularity and established itself as a crucial instrument for analyzing public opinion on elections and other trending issues. The unexpected but interesting results of recently held Nigeria's presidential election shifted attention to the upcoming governorship race in Lagos State. In this work, we propose a Google’s Bidirectional Encoder Representations from Transformers (BERT) model for the sentiment analysis of governorship election in Lagos State, Nigeria, using Twitter data. A total of 800,000 personal and public tweets were scraped from twitter concerning the three prominent contesting Lagos State Gubernatorial candidates using carefully selected search queries. The tweets were preprocessed to avoid noise and inconsistencies and the preprocessed tweets were parsed into the pre-trained and finetuned BERT model. The result was analyzed to establish the sentiments of the public about the candidates. The social networks of the candidates were also presented and the effect of parameter using different learning rates (LR) was also considered. The BERT model achieved the maximum performance under varied learning rate and epoch sizes of 88% precision, 92% recall and 91% F1-Measure. Results also showed that the learning rate at 1e-7 gave the best performance. Also, the smaller the learning rate, the higher the accuracy but the larger the epoch size, the higher the accuracy. Applying the developed BERT model to the public’s tweet showed that the election will be a two-party race between the Labour Party and All Progressives Congress party, thereby challenging the status quo. The results of the experiment demonstrated that sentiment analysis and other Natural Language Processing activities can help with comprehension of the social media environment. Results also showed how much influence each candidate has over the outcome of the election. We come to the conclusion that estimating election results and providing insights for electoral parties can benefit from sentiment analysis and other language models.