Mining call center dialog data

Abstract

We consider the problem of mining conversations between customers and call center representatives for automatically classifying calls into predefined categories. We analyze the conversations for speaker dependent information content using several multi-class classification technologies. The data consists of 539 manually transcribed conversations belonging to 15 categories. Classifiers were built using Support Vector Machines, Naive Bayes, Latent Semantic Analysis, Vector Space and K-Nearest Neighbor technologies. SVM classifiers were found to perform consistently well giving an accuracy of about 74% on the entire data and about 92% when considering only the 4 largest classes. It is observed that very high weightage to either the customer part of the dialog or that of the agent results in poor accuracy. Nearly equal weightage to the customer and agent provides the best results consistently. This approach has potential to identify cross sell and up-sell opportunities in real-time.