A Machine Learning Approach for Multi-Label Classification in Candidate Election Social Media Analysis

Abstract

Multi-label text classification in social media comments presents a significant challenge in natural language processing. Several previous studies have conducted sentiment analysis on candidate (presidential and gubernatorial) elections using machine learning approaches. However, an opinion can contain more than one category or label simultaneously, such as sentiment, candidate, or certain issues. This study proposes a multi-label classification model to improve accuracy, addressing challenges such as complex language structure, non-standard word usage, and imbalanced data. The proposed model is compared with three popular classification algorithms: Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN), for handling multi-label text classification tasks. The proposed model comprises a classification pipeline that includes data preprocessing, feature extraction using TF-IDF, and the integration of the GridSearchCV technique to enhance algorithm performance and effectiveness. The evaluation is conducted using multi-label metrics such as Precision, Recall, and F1-Score. The experiment results showed that SVM with GridSearchCV provided the best performance in terms of precision and generalization on the gubernatorial election dataset. SVM + GridSearchCV yielded scores of 97.4% and 99.2% for candidate labels, and 99.2% and 99.0% for sentiment labels. While NB and KNN also showed improvements, their performance was not as significant as SVM. NB outperformed in computational performance, whereas KNN demonstrated poor performance on high-dimensional data.