USING SUPPORT VECTOR MACHINE TO PREDICT POLITICAL AFFILIATIONS ON TWITTER: SUPERVISED MACHINE LEARNING APPROACH

Abstract

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.