A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues

Galitsky, Boris A. and Gonzalez, M. and Chesnevar, C. (2009) A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues. Decision Support Systems 46 (3), pp. 717-729. ISSN 0167-9236.

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Official URL: http://dx.doi.org/10.1016/j.dss.2008.11.015

Abstract

Automating customer complaints processing is a major issue in the context of knowledge management technologies for most companies nowadays. Automated decision-support systems are important for complaint processing, integrating human experience in understanding complaints and the application of machine learning techniques. In this context, a major challenge in complaint processing involves assessing the validity of a customer complaint on the basis of the emerging dialogue between a customer and a company representative. This paper presents a novel approach for modelling and classifying complaint scenarios associated with customer-company dialogues. Such dialogues are formalized as labelled graphs, in which both company and customer interact through communicative actions, providing arguments that support their points. We show that such argumentation provides a complement to perform machine learning reasoning on communicative actions, improving the resulting classification accuracy.

Metadata

Item Type:	Article
Keyword(s) / Subject(s):	Automated decision making, automated complaint processing, argumentative dialogues, pattern matching
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Administrator
Date Deposited:	28 Jul 2011 10:17
Last Modified:	09 Aug 2023 12:30
URI:	https://eprints.bbk.ac.uk/id/eprint/3901

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