From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election.

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Two million tweets with 18 features were collected from Twitter containing public and personal tweets of the three top contestants - Atiku Abubakar, Peter Obi and Bola Tinubu - in the forthcoming 2023 Presidential election. Sentiment analysis was performed on the preprocessed dataset using three 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. This study spanned ten weeks starting from the candidates' declaration of intent to run for Presidency. Results: The sentiment models gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2%, 87.6% and 82.9% respectively for LSTM; 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively for BERT and 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively for LSVC. Result also showed that Peter Obi has the highest total impressions the highest positive sentiments, Tinubu has the highest network of active friends while Atiku has the highest number of followers. Conclusion: Sentiment analysis and other Natural Language Understanding tasks can aid in the understanding of the social media space in terms of public opinion mining. We conclude that opinion mining from Twitter can form a general basis for generating insights for election as well as modeling election outcomes.

A machine learning prediction of academic performance of secondary school students using radial basis function neural network.

BACKGROUND: Predictive models for academic performance forecasting have been a useful tool in the improvement of the administrative, counseling and instructional personnel of academic institutions. AIM: The aim of this work is to develop a Radial Basis Function Neural Network for prediction of students' performance using their past academic records as well as their cognitive and psychomotor abilities. METHODS: We obtained data from a secondary school repository containing academic, cognitive and psychomotor scores of the students. The preprocessed dataset was used to train the RBFNN model. The impact of Principal Component Analysis on the model performance was also measured. RESULTS: The results gave a sensitivity (pass prediction) of 93.49%, specificity (failure prediction) of 75%, overall accuracy of 86.59% and an AUC score (aggregate measure of performance across the possible classification thresholds) of 94%. CONCLUSION: We established in this study that psychomotor and cognitive abilities also predict students' performance. This study helps students, parents and teachers to get a projection of academic success even before sitting for the examination.

Breast cancer risk prediction in African women using Random Forest Classifier.

INTRODUCTION: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate. AIM: In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique. METHODS: A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model. RESULTS: We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Chi-Square selected features. CONCLUSION: Random Forest Classifier has a good potential at predicting the risk of breast cancer in African women. The study helped to identify the risk factors of breast cancer in African women. This is a valuable information which can help African women to pay attention to those risk factors with the intention of reducing the incidence of breast cancer in Africa.