Based on real world user demands, we demonstrate how animated visualisation of evolving text corpora displays the underlying dynamics of semantic content. To interpret the results, one needs a dynamic theory of word meaning. We suggest that conceptual dynamics as the interaction between kinds of intellectual, emotional etc. content, and language, is key for such a theory. We demonstrate our methodology by two-way seriation which is a popular technique to analyse groups of similar instances and their features, as well as the connections between the groups themselves. The two-way seriated data may be visualised as a two-dimensional heat map or as a three-dimensional landscape where colour codes or height correspond to the values in the matrix. In this paper we focus on two-way seriation of sparse data in the Reuters-21568 test collection. To achieve a meaningful visualisation thereof we introduce a compactly supported convolution kernel similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional sparse space with values that interpolate nearby elements, and provides insight into the clustering structure. We also extend two-way seriation to deal with online updates of both the row and column spaces, and, combined with the convolution kernel, demonstrate a three-dimensional visualisation of dynamics.