The paper introduces new features for describing possible focus variation in a human/human conversation. The application considered is a real-life telephone customer care service. The purpose is to hypothesize the dominant theme of conversations between a casual customer calling. Conversations are processed by an automatic speech recognition system that provides hypotheses used for extracting word frequency. Features are extracted in different, broadly defined and partially overlapped, time segments. Combinations of each feature in different segments are represented in a quaternion algebra framework. The advantage of the proposed approach is made evident by the statistically significant improvements in theme classification accuracy .