Rama Akkiraju, Pinar Keskinocak, et al.
Applied Intelligence
Business-oriented conversations between customers and agents need to be analyzed to obtain valuable insights that can be used to improve product and service quality, operational efficiency, and revenue. 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 then use text mining to extract important associations between key entities (insights). We show the effectiveness of the method for making chance discoveries by using real life data from a car rental service center. © 2008 Elsevier Inc. All rights reserved.
Rama Akkiraju, Pinar Keskinocak, et al.
Applied Intelligence
Benjamin N. Grosof
AAAI-SS 1993
Guojing Cong, David A. Bader
Journal of Parallel and Distributed Computing
Amarachi Blessing Mbakwe, Joy Wu, et al.
NeurIPS 2023