Hybrid support vector machine and random forest for environmental issue sentiment analysis on social media x

Abstract

This study investigates the effectiveness of a hybrid machine learning model combining Support Vector Machine (SVM) and Random Forest (RF) for sentiment analysis of environmental issues discussed on social media X. A quantitative experimental research design was employed using textual data related to environmental topics collected through an application programming interface (API)-based data extraction process. Prior to model development, the collected data underwent a series of preprocessing procedures, including text normalization, tokenization, stopword elimination, and stemming. The processed text was then converted into numerical feature vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. To assess classification performance, three models were implemented and compared: Support Vector Machine, Random Forest, and a hybrid SVM–RF ensemble model. Model evaluation was conducted through cross-validation using accuracy, precision, recall, and F1-score as performance indicators. The experimental results revealed that the hybrid model achieved the best overall performance, attaining an accuracy of 89.10%, compared with 85.30% for Random Forest and 82.45% for Support Vector Machine. In addition, the hybrid approach generated higher precision, recall, and F1-score values, demonstrating greater robustness and consistency in sentiment classification. These findings suggest that integrating multiple machine learning algorithms can significantly enhance the analysis of complex and unstructured social media data concerning environmental issues.