V izi T ex: Interactive Visual Sense-Making of Text Corpora (2021)

Abstract: Information visualization is critical to analytical reasoning and knowledge discovery. We present an interactive studio that integrates perceptive visualization techniques with powerful text analytics algorithms to assist humans in sense-making of large complex text corpora. The novel visual representations introduced here encode the features delivered by modern text mining models using advanced metaphors such as hypergraphs, nested topologies and tessellated planes. They enhance human-computer interaction experience for various tasks such as summarization, exploration, organization and labeling of documents. We demonstrate the ability of the visuals to surface the structure, relations and concepts from documents across different domains.

## introduction despite admirable progress in machine learning, human participation in data analysis and decision making is a reality. human efforts are often required for bootstrapping labels, interpreting decisions and verifying outcomes. it is important to design intuitive visualizations that can exploit the pattern recognition and spatial reasoning capabilities of humans in order to transform the humancomputer interaction experience. while traditional bar charts and heat map displays hold value, complex interactive graphical representations @xcite are often required to effectively slice and dice high-dimensional data. furthermore, it is essential for these visuals to encode all the features delivered by machine learning models. interactive information processing in large complex text corpora pose a significant challenge due to the sheer volume, lack of structure and multifaceted nature of text material. existing efforts around visual interfaces for sense-making of text documents do not characterize the true potential of the text analytics algorithms @xcite , are often tied to a particular model @xcite or remain fragmented with task specific solutions @xcite . there is a compelling opportunity for perceptive visualization techniques that fully leverage the capabilities of text mining models and cater to analysis at various levels of task granularity and human expertise. towards this effort, we propose an interactive studio that delivers novel visual representations for common text oriented tasks such as theme discovery, document organization and label exploration. visualizations presented here include a hypergraph that encodes distributional similarity between words, a multi-level radial layout to capture distinguishing terms, a clutter-free parallel coordinate plot of topic relations, a nested topology for document hierarchies and a tessellated plane to capture boundary points. these visualizations highlight interesting linguistic patterns in the corpus, surface complex relations between documents and reduce the burden of annotations for labeling exercises. the structure of the framework, which follows a loosely coupled architecture pattern, is outlined in fig. 1 . there are three main components that drive the system: a text corpora, a suite of text analytics algorithms and a set of visualization techniques. we particularly focus on metadata rich corpus with multiple facets or data dimensions along which a corpus can be subdivided. the visualizations are independent of the analytical models and newer algorithms can be flexibly plugged-in. all the generated graphical elements are interactive, with the end user being able to zoom, pan, hover and click for receiving contextual information. the users merely require a web-browser to access the visuals. we demonstrate the domain agnostic nature of the visuals by providing illustrations from publicly available datasets that span across informal, legal and scientific language formats. in the following sections, we review related efforts and present eight different visualizations. ## related work research in visual text analytics has gained prominence and surveys such as @xcite provide an overview of recent progress. differently, @xcite present a visual survey by collating the images generated by the various visualization methods and offer an interactive filter for exploration. there has been several efforts towards the development of software tools for analyzing text data. for example, the leximancer @xcite application plots word frequency statistics to help an analyst examine concepts in text. tiara @xcite is a visual text analysis tool that uses topic models to summarize documents. the popular pyldaviz package (sievert and shirley, 2014) offers interactive visualization for topic models. our work differs from these by introducing new metaphors and integrating a variety of text mining tasks. designing interactive graphics for the creation of interesting visualization techniques is popular. storyprint @xcite is a visualization method for script-based media that presents prominence and emotion of characters in a scene. the visual analytic system in verifi @xcite enables investigation of misinformation on social media. @xcite introduced a multi-scale visualization tool to illustrate the inner workings of attention patterns generated by neural transformers. unlike these application and model specific efforts, our work is intended to be agnostic both to the data domain and underlying algorithm. ## visualizations we present several techniques for visually analyzing a corpus at word, topic and document levels below. samples from three different datasets namely amazon reviews (mcauley and leskovec, 2013), arxiv abstracts foot_0 and code of federal regulations (cfr) foot_1 are used to illustrate the visualizations. ## conclusion large and complexly related text collections require perceptive information visualization techniques to assist human understanding and reasoning. the interactive visualizations proposed here facilitates discovering concepts, themes, clusters, outliers and structure in a corpus by integrating text analytics models with novel visual representations. in future, we wish to extend the suite of statistical models for selection and incorporate new visuals for temporal analysis that exploits animations.