Data-driven decision-making method for determining the handling department for online appeals

Abstract

Purpose

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

Design/methodology/approach

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Findings

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

Originality/value

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

Citation

Chen, S.-Q., You, T. and Zhang, J.-L. (2024), "Data-driven decision-making method for determining the handling department for online appeals", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-04-2024-1050