Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps

Abstract Developing a comprehensive explanation of complex social phenomena is a difficult task that analysts often have to perform using vast collections of text documents. On the one hand, solutions exist to assist analysts in creating causal maps from text documents, but these can only articulate the relationships at work in a problem. On the other hand, Fuzzy Cognitive Maps (FCMs) can articulate these relationships and perform simulations, but no environment exists to help analysts in iteratively developing FCMs from text. In this paper, we detail the design and implementation of the first tool that allows analysts to develop FCMs from text collections, using interactive visualizations. We make three contributions: (i) we combine text mining and FCMs, (ii) we implement the first visual analytics environment built on FCMs, and (iii) we promote a strong feedback loop between interactive data exploration and model building. We provide two case studies exemplifying how to create a model from the ground-up or improve an existing one. Limitations include the increase in display complexity when working with large collection of files, and the reliance on KL-divergence for ad-hoc retrieval. Several improvements are discussed to further support analysts in creating high-quality models through interactive visualizations.