Word Clouds with Latent Variable Analysis for Visual Comparison of Documents

Abstract

Word cloud is a visualization form for text that is recognized for its aesthetic, social, and analytical values. Here, we are concerned with deepening its analytical value for visual comparison of documents. To aid comparative analysis of two or more documents, users need to be able to perceive similarities and differences among documents through their word clouds. However, as we are dealing with text, approaches that treat words independently may impede accurate discernment of similarities among word clouds containing different words of related meanings. We therefore motivate the principle of displaying related words in a coherent manner, and propose to realize it through modeling the latent aspects of words. Our WORD FLOCK solution brings together latent variable analysis for embedding and aspect modeling, and calibrated layout algorithm within a synchronized word cloud generation framework. We present the quantitative and qualitative results on real-life text corpora, showcasing how the word clouds are useful in preserving the information content of documents so as to allow more accurate visual comparison of documents.