  Customer reviews play a crucial role in assessing customer satisfaction,
gathering feedback, and driving improvements for businesses. Analyzing these
reviews provides valuable insights into customer sentiments, including
compliments, comments, and suggestions. Text classification techniques enable
businesses to categorize customer reviews into distinct categories,
facilitating a better understanding of customer feedback. However, challenges
such as overfitting and bias limit the effectiveness of a single classifier in
ensuring optimal prediction. This study proposes a novel approach to address
these challenges by introducing a stacking ensemble-based multi-text
classification method that leverages transformer models. By combining multiple
single transformers, including BERT, ELECTRA, and DistilBERT, as base-level
classifiers, and a meta-level classifier based on RoBERTa, an optimal
predictive model is generated. The proposed stacking ensemble-based multi-text
classification method aims to enhance the accuracy and robustness of customer
review analysis. Experimental evaluations conducted on a real-world customer
review dataset demonstrate the effectiveness and superiority of the proposed
approach over traditional single classifier models. The stacking ensemble-based
multi-text classification method using transformers proves to be a promising
solution for businesses seeking to extract valuable insights from customer
reviews and make data-driven decisions to enhance customer satisfaction and
drive continuous improvement.
