new_data

This study used a benchmark dataset, applying different embedding like LASER and FastText to capture contextual information, which was combined to create a new hybrid embedding. This hybrid embedding was fed to machine-learning (ML) and deep learning (DL) classifiers. ML classifiers consist of three ensembles: bagging utilizes Random Forest (RF), boosting with Light Gradient Boosting Machine (LGBM) and Xtreme Gradient Boosting (XGB), and stacking with Support Vector Machine (SVC) and XGB as base learners and Logistic Regression (LR) as the Meta classifier. DL classifiers such as fully connected network (FCN), Convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) have been employed. The model's performance was assessed through independent set and k-fold testing with 5 and 10 folds, using evaluation metrics such as Accuracy, Recall, Precision, and F1 score. In experiments, DL classifiers have outperformed ML classifiers regarding accuracy score. The proposed model outperformed previous benchmark studies, achieving an Accuracy of 0.86, Recall of 0.901, Precision of 0.866, and F1 score of 0.883 in the independent set test. This method contributes to NLP research by showcasing the utility of a hybrid embedding approach.