TopicInk: Visualizing Disaster-Related Textual Data Using LDA Topic Modeling: Vast Challenge 2019: Honorable Mention for Clear Articulation of Methodology

Chen Guo, Xiang Liu, Evie Cai, Yingjie Victor Chen, Zhenyu Cheryl Qian, Rui Li

Abstract: We proposed a visual analytics tool to analyze social media posts through machine-learning techniques. Latent Dirichlet Allocation and Named Entities Recognition were used to extract semantic information. 12 topics were identified to interpret semantic meanings and reveal spatiotemporal patterns in the dataset. Our visualization consists of topic bubbles, frequency bar chart, stream graph, fisheye list, massage view, map view, word cloud, and social network graph. All the views are linked together and enhanced by efficient interactions. This paper describes the methodology and visualization design in detail.