Performing and Visualizing Temporal Analysis of Large Text Data Issued for Open Sources: Past and Future Methods

Abstract

In this paper we first propose a state of the art on the methods for the visualization and the interpretation of textual data, in particular of scientific data. We then shortly present our contributions to this field in the form of original methods for the automatic classification of documents and easy interpretation of their content through characteristic keywords and classes created by our algorithms. In a second step, we focus our analysis on the data evolving over time. We detail our diachronic approach, especially suitable for the detection and visualization of topic changes. This allows us to conclude with Diachronic'Explorer, our upcoming tool for visual exploration of evolutionary data.

WCLOUDVIZ: Word Cloud Visualization of Indonesian News Articles Classification Based on Latent Dirichlet Allocation

“…The third design is tree design. This design is a popular design for visualizing LDA's output such as relationship-enriched visualization [19], Diachronic visualization [20], LDAExplore [21], and etc. LDAExplore also implements parallel coordinates to show topic proportion for each document.…”

“…A line is drawn to connect the document's ID and related topic proportions for each remaining vertical line. The last design is force-directed graphs, such as relationship-enriched visualization [19] and Diachronic visualization [20]. In addition, there are some visualizations that have been developed for broader topic (information retrieval) such as map view, tree view, and bubble view which are implemented in [22], graph-view [23], heat map, interactive stream graph, and context focus which are implemented in [24].…”