Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles.

当对文本信息的集合执行有意义的任务时，分析人员面临着许多严峻的挑战，这些任务要比没有计算协助就可以进行合理分析的更大。为了扩大这样的感官任务，需要新的方法将人类认知感官活动与机器学习进行交互集成。为了实现这一目标，我们提供了一个在环计算模型，该模型反映了人类的感知过程，并由觅食和综合子过程组成。我们将综合循环建模为交互式空间投影，并将觅食循环建模为与主题建模相结合的交互式相关性排名。我们使用语义交互将感官过程的这两个部分结合起来，从而使人类 的空间合成动作被转换为自动搜寻和新的相关信息的合成。归根结底，由于分析师的综合活动，该模型的搜寻能力使与大文本数据的交互更加容易和高效，从而提高了分析师的判断能力。我们讨论了交互意义模型背后的交互设计和理论。该模型体现在名为Cosmos的新型视觉分析原型中，该模型中的分析师通过直接与降维空间进行交互以表达数据子集上的关系，从而在较大语料库中合成了结构。然后，我们演示Cosmos如何以一种现实的场景来支持有意义的任务，该场景可以调查阿德莱德自然灾害的影响，