Optimizing Multi-Class Text Classification: A Diverse Stacking Ensemble Framework Utilizing Transformers

TL;DR

This paper introduces a novel stacking ensemble framework utilizing multiple transformer models to improve multi-class text classification accuracy for customer reviews, addressing overfitting and bias issues in single classifiers.

Contribution

It proposes a new ensemble method combining BERT, ELECTRA, DistilBERT, and RoBERTa to enhance classification robustness and accuracy in customer review analysis.

Findings

Outperforms traditional single classifier models in accuracy

Demonstrates robustness across real-world datasets

Enhances insights extraction from customer reviews

Abstract

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.