Automatic identification of important segments and expressions for mining of business-oriented conversations at contact centers

Abstract

Textual records of business-oriented conversations between customers and agents need to be analyzed properly to acquire useful business insights that improve productivity. For such an analysis, it is critical to identify appropriate textual segments and expressions to focus on, especially when the textual data consists of complete transcripts, which are often lengthy and redundant. In this paper, we propose a method to identify important segments from the conversations by looking for changes in the accuracy of a categorizer designed to separate different business outcomes. We extract effective expressions from the important segments to define various viewpoints. In text mining a viewpoint defines the important associations between key entities and it is crucial that the correct viewpoints are identified. We show the effectiveness of the method by using real datasets from a car rental service center. © 2007 Association for Computational Linguistics.