Application of Support Vector Machine for Effective Prediction of Election for Sentiment Analysis

Abstract

This study proposes the use of machine learning models, namely Support Vector Machine (SVM), for effective sentiment analysis on a dataset from the Kaggle repository. Considering the Tinubu 2023 election dataset, it can be seen that SVM having been fed with the cleansed dataset feature obtained an accuracy score of 93.2%, considering the result of each algorithm on the 2023 Nigerian election datasets. The study investigates data preprocessing techniques, feature selection, and correlation metrics to optimize the sentiment detection process. Results show that the SVM model achieves the highest accuracy, making it a potential tool for political analysis, business marketing, and public policy implementation. However, future research may explore deep learning techniques and data balancing strategies to enhance the models' performance further.