Demonstrating conceptual dynamics in an evolving text collection

Based on real‐world user demands, we demonstrate how animated visualization 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 and emotional content and language is key for such a theory. We demonstrate our method by two‐way seriation, which is a popular technique to analyze groups of similar instances and their features as well as the connections between the groups themselves. The two‐way seriated data may be visualized as a two‐dimensional heat map or as a three‐dimensional landscape in which color codes or height correspond to the values in the matrix. In this article, we focus on two‐way seriation of sparse data in the Reuters‐21568 test collection. To achieve a meaningful visualization, 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 visualization of dynamics.