Abstract We present an analysis and visualization method for computing what distinguishes a given document collection from others. We determine topics that discriminate a subset of collections from the remaining ones by applying probabilistic topic modeling and subsequently approximating the two relevant criteria distinctiveness and characteristicness algorithmically through a set of heuristics. Furthermore, we suggest a novel visualization method called DiTop‐View, in which topics are represented by glyphs (topic coins) that are arranged on a 2D plane. Topic coins are designed to encode all information necessary for performing comparative analyses such as the class membership of a topic, its most probable terms and the discriminative relations. We evaluate our topic analysis using statistical measures and a small user experiment and present an expert case study with researchers from political sciences analyzing two real‐world datasets.

Abstract This paper presents a novel Visual Analytics approach that helps linguistic researchers to explore the world's languages with respect to several important tasks: (1) The comparison of manually and automatically extracted language features across languages and within the context of language genealogy, (2) the exploration of interrelations among several of such features as well as their homogeneity and heterogeneity within subtrees of the language genealogy, and (3) the exploration of genealogical and areal influences on the features. We introduce theWorld'sLanguagesExplorer, which provides the required functionalities in one single Visual Analytics environment. Contributions are made for different parts of the system: We introduce an extended Sunburst visualization whose so‐called feature‐rings allow for a cross‐comparison of a large number of features at once, within the hierarchical context of the language genealogy. We suggest a mapping of homogeneity measures to all levels of the hierarchy. In addition, we suggest an integration of information from the areal data space into the hierarchical data space. With our approach we bring Visual Analytics research to a new application field, namely Historical Comparative Linguistics, and Linguistic and Areal Typology. Finally, we provide evidence of the good performance of our system in this area through two application case studies conducted by domain experts.

This article describes automatic methods and interactive visualizations that are tightly coupled with the goal to enable users to detect interesting portions of text document streams. In this scenario the interestingness is derived from the sentiment, temporal density, and context coherence that comments about features for different targets (e.g., persons, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization, an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for feature-based sentiment exploration over time.

Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.