University of North Carolina, Charlotte, NC, USA.
IEEE Trans Vis Comput Graph. 2012 Jan;18(1):93-105. doi: 10.1109/TVCG.2010.225.
Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.
许多带有时间引用的文本集合，如新闻语料库和博客，都是为了报道和讨论现实生活中的事件而生成的。因此，在探索这些文本集合时，与事件相关的任务（如检测驱动文本文档生成的现实生活事件、跟踪事件的演变以及调查有关感兴趣事件的报道和评论）非常重要。为了整合和利用人类的努力来完成这些任务，我们提出了一种名为 EventRiver 的新的可视化分析方法。EventRiver 将基于事件的自动化文本分析和可视化相结合，以揭示激发文本生成的事件以及它们构建的长期故事。在可视化方面，用户可以交互地进行事件浏览、跟踪、关联和调查等任务。我们已经为探索新闻语料库实现了 EventRiver 的工作原型。我们进行了一组案例研究、实验和初步用户测试，以评估其有效性和效率。
