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

Multi-label text classification in social media comments presents a signi icant challenge in natural language processing. Several previous studies have conducted sentiment analysis on candidate (presidential and guber- natorial) 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 classica- tion 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 outper- formed in computational performance, whereas KNN demonstrated poor performance on high-dimensional data.