T e M o T opic: Temporal Mosaic Visualisation of Topic Distribution, Keywords, and Context (2021)

Abstract: In this paper we present TeMoTopic, a visualization component for temporal exploration of topics in text corpora. TeMoTopic uses the temporal mosaic metaphor to present topics as a timeline of stacked bars along with related keywords for each topic. The visualization serves as an overview of the temporal distribution of topics, along with the keyword contents of the topics, which collectively support detail-on-demand interactions with the source text of the corpora. Through these interactions and the use of keyword highlighting, the content related to each topic and its change over time can be explored.

## introduction many text corpora, such as news articles, are temporal in nature, with the individual documents distributed across a span of time. as the size and availability of text corpora have continued to increase in recent years, effective analysis of the content of corpora has become challenging. taking the temporal nature of most corpora into account when analysing the text makes it more difficult to describe the corpora and to interpret intuitively the results of analysis. topic modeling techniques, such as latent dirichlet allocation (lda) @xcite , have been used to automatically generate topic groups in text corpora. these topics can help in understanding the contents of a corpus by using keywords and topic association probabilities generated by the topic modelling technique. however, interpreting the results of the techniques is not always easy, and the results can seem counter-intuitive when looking only at the weighted keyword lists. therefore, visualization techniques have been used extensively to help with the interpretation of the large number of topics generated by these models. the same is true of temporal topic modeling techniques, such as dynamic topic modeling @xcite , which require additional visualization techniques to aid intuitive understanding of the temporal segmentation of the topics and their related keywords. in this paper, we propose temotopic as a contribution to the collection of visualization techniques for exploring the temporal distribution of topics in text corpora through the use of temporal mosaics. temotopic adopts a space-filling approach to show topic distribution over time, and presents keywords related to each topic at the overview level of the visualization. the visualization is interactive and, in contrast to many other techniques, enables direct investigation of the source documents associated with individual topics and keywords. this allows the user to get a general sense of the meaning of a topic through its associated keywords, as well as providing the ability to dive into the details of the related documents. ## temotopic: temporal mosaic topic visualization figure 1 shows the temotopic visualization tool. it consists of two juxtaposed views @xcite : the temporal mosaic (left), and the document view (right). the design of the temporal mosaic is based on a visualization proposed by @xcite , and further expanded in our previous temporal mosaic visualizations temoco visualization @xcite and temoco-doc visualization @xcite , which have been used to link transcripts of meetings to document reports in a medical context. ## conclusions while many other temporal visualization techniques, such as themeriver @xcite , offer some of the functionality for temporal visualization of topics or visualization of content changes, they do not feature implicit linking between the visualization and the underlying content documents. we consider this to be the main contribution of temotopic visualization and its distinguishing feature with regards to the state of the art. as such, determining the necessity and validity of this approach in the identified domain is an important step before further development of the visualization prototype. future work will, therefore, include evaluating the usability of a future iteration of the system with domain experts in both news analysis and topic modelling.