Introduction to the Special Section on Intelligent Visual Interfaces for Text Analysis

Text analysis technologies aim at automatically extracting knowledge and uncovering information buried under seas of text documents, including academic publications, news articles, emails, and patient records. Complex analysis results of these technologies, however, are often difficult for average users to digest and leverage. Furthermore, such results may be inaccurate or contain ambiguous or even misleading information. This special section presents several visual interfaces for text analytics technologies designed to help users better consume text analysis results. In the past, most work focused on either using basic visualization (e.g., bar charts and pie charts) to present the final results of complex text analysis or inventing visual metaphors to illustrate the results of simple text analysis (e.g., tf-idf measure of keywords). Based on the type of information visualized, these systems have been classified into two categories: metadata-based and content-based text visualization. Metadata-based text visualizations focus on visualizing the metadata of text documents. For example, in email analysis, there are thread-based email visualizations [Kerr 2003; Venolia and Neustaedter 2003] and relationship-based visualizations of email senders and receivers (e.g., Nardi et al. [2002] and Perer and Smith [2006], www.enronexplorer.com, and jheer.org/enron). Similarly, in text search, document metadata such as document length and query term frequency can be visualized [Hearst 1995]. Metadata-based visualization aids in text analysis are inadequate in uncovering deeper insights buried in the text; they especially do not work for documents with little associated metadata. Thus, researchers have developed content-based text visualization. For example, Viegas et al. [2006] use Themail to visualize keywords based on their TF-IDF scores in an email collection. Similarly, Strobelt et al. [2009] use a mixture of images and TF-IDF-based keywords to create a compact visualization of a document. There are also many other general-purpose text visualization systems that transform a collection of text into a visual illustration [Wise et al. 1995]. These systems include the Galaxy of News [Rennison 1994], Jigsaw [Stasko et al. 2008], and WordTree [Wattenberg and Viegas 2008]. While text analysis and text visualization have separately received much attention, little work has focused on tightly integrating interactive visualization with text mining techniques for information exploration and scalable decision support. This is an important research field that directly addresses real-world problems in many domains, including but not limited to business intelligence, fraud detection, emergency management, customer relationship management, market intelligence, and healthcare analytics. For this reason, it has started to attract the attention of both academia and industry and will continue to do so in the future. To highlight the work in this emerging area, this special section includes seven excellent articles from the leading researchers in the field. These articles cover most key aspects of visual text analytics, including interactive text search and clustering, text browsing driven by user interests, as well as interactive exploration of large text collections with topic modeling and sentiment analysis. In “Watch the Story Unfold with TextWheel: Visualization of Large-Scale News Streams”, Cui et al. study how interactive visualization can help users understand keyword-based searching and clustering of news articles. The proposed TextWheel system aims at conveying multiple attributes of news articles and the macro/micro relationships between news streams and keywords in context. In “Visual Abstraction and Ordering in Faceted Browsing of Text Collections”, Thai et al. consider facet navigation