This paper presents two approaches to visually analyze the topic shift of a pool of documents over a given period of time. The first of the proposed methods is based on a multi-dimensional scaling algorithm, which places vectors representing terms occurring in certain years (period-frequency-vectors) in a spatial, two-dimensional space. This kind of visualization enables the detection of terms occurring in documents, published in particular years, or terms spread over different years.
The second method uses a graph based approach. Publishing dates of documents, as well as their terms are represented by the vertices of a graph. Terms related to a specific publishing year are connected to the vertex of the year via an edge. By usage of activation spreading techniques, terms frequently occurring in documents published in particular years can be discovered visually.
The authors tested both approaches with 2431 abstracts of papers published in the IEEE Transactions on SMC-A, SMC-B, and SMC-C in the years 1996 to 2006. This experiment indicates that a number of interesting terms can be nicely separated in clumps according to individual years or periods of time. In addition, one can visualize the emergence of specific terms over certain periods of time and how these and other terms fade away again later.