  This paper deals with the automatic analysis of conversations between a
customer and an agent in a call centre of a customer care service. The purpose
of the analysis is to hypothesize themes about problems and complaints
discussed in the conversation. Themes are defined by the application
documentation topics. A conversation may contain mentions that are irrelevant
for the application purpose and multiple themes whose mentions may be
interleaved portions of a conversation that cannot be well defined. Two methods
are proposed for multiple theme hypothesization. One of them is based on a
cosine similarity measure using a bag of features extracted from the entire
conversation. The other method introduces the concept of thematic density
distributed around specific word positions in a conversation. In addition to
automatically selected words, word bi-grams with possible gaps between
successive words are also considered and selected. Experimental results show
that the results obtained with the proposed methods outperform the results
obtained with support vector machines on the same data. Furthermore, using the
theme skeleton of a conversation from which thematic densities are derived, it
will be possible to extract components of an automatic conversation report to
be used for improving the service performance. Index Terms: multi-topic audio
document classification, hu-man/human conversation analysis, speech analytics,
distance bigrams
