Bridging Text Visualization and Mining: A Task-Driven Survey

Shi‐Xia Liu, Xiting Wang, Christopher Collins, Wenwen Dou, Fangxin Ouyang, Mennatallah El‐Assady, Liu Jiang, Daniel A. Keim

IEEE Transactions on Visualization and Computer Graphics · 2018 · 106 citations · 350 references

Concepts

TL;DR

Visual text analytics has recently emerged as a prominent topic in both academic research and the commercial world. The study reviews 263 visualization and 4,346 mining papers from 1992‑2017 to map techniques, tasks, and their interrelations, aiming to help researchers understand common concepts, explore relationships, assess current tool development practices, identify research opportunities, and apply the approach to other interdisciplinary areas. The authors performed a systematic analysis of 263 visualization and 4,346 mining papers, extracted co‑occurrence relationships among concepts, and built a web‑based visualization tool to explore research trends and opportunities. The analysis yielded approximately 300 concepts and a taxonomy for each type—visualization techniques, mining techniques, and analysis tasks.

Abstract

Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.

References

350