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Vol. 5 No. 1 (2024)
2024 Edition

Articles

Comparative Evaluation of Multi-dimensional Annotation Frameworks for Customer Feedback Analysis: A Cross-industry Approach

Published 2024-11-28

  • Jiayu Liang
    • ,
  • Zhuxuanzi Wang

Jiayu Liang
Applied Statistics, Cornell University, NY, USA

Zhuxuanzi Wang
Information Systems, Cornell Tech, NY, USA

Abstract

This paper presents a novel multi-dimensional annotation framework for customer feedback analysis across diverse industry contexts. The research addresses the limitations of traditional single-dimension annotation approaches by introducing a hierarchical classification structure encompassing content type, sentiment expression, customer intent, issue urgency, and resolution status dimensions. The proposed framework employs a modular architecture with specific cross-industry adaptation mechanisms, enabling consistent methodological application while accommodating domain-specific requirements. Experimental validation was conducted across telecommunications, e-commerce, financial services, and technology support sectors using a dataset of 33,784 customer communications. The multi-dimensional approach demonstrated significant performance advantages over single-dimension classification schemes, achieving an average improvement of 12.6% across evaluation metrics. Telecommunications implementations achieved the highest overall performance (F1=0.87), while specific dimension effectiveness varied across industries. The hierarchical classification structure provided particular benefits for complex multi-topic communications, with 18.4% higher accuracy compared to flat classification approaches. The framework demonstrated practical implementation value across customer experience management, product development, and quality assurance applications, with organizations reporting average time savings of 37% in feedback analysis workflows. The study extends previous research on argumentation schemes classification and customer service emotional perception analysis by providing a comprehensive approach to capturing the multi-faceted nature of customer communications while maintaining implementation feasibility.

Keywords

Multi-dimensional annotation framework, Customer feedback analysis, Cross-industry adaptation, Hierarchical classification

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