Early work on call centers focused on opinion collection, and service acceptance used manual entry to record text information for incoming calls and to count various types of calls. With the development of text mining technologies, it is possible for call centers to become information providers. Although traditional text mining methods have been well studied, no algorithm has been designed specifically for call center data, which contains structured information about the call center in addition to plain text. A telephone call usually has several properties such as caller number, call time, and so on. Aiming at the data characteristics of call centers, in this paper, we propose an improved latent Dirichlet allocation (LDA) model called A-LDA, which includes information from external-related attributes and is suitable for building topic mining models for call centers. We present a Gibbs sampling-distributed computing implementation for our model's inferences and use datasets from China Central Television telephone call center to perform an experiment on topic detection. The results show that A-LDA, which uses external correlation properties, has a lower perplexity value and a better generalization performance than the traditional LDA method and can find topics associated with values contained by the external attributes. Copyright (C) 2016 John Wiley & Sons, Ltd.