Shape-aware Word Cloud Generation Method

In the big data era,we have a lot of text data.How to make better text information visualization has become an important problem.Word cloud is an important means for visualizing text.In the past years,shape-bounded word cloud became very popular on social media.However,existing technology ignores the original proportional relationship between words and their frequency in order to get a high filling rate,misleading people in acquiring the word frequency information in the word cloud.We present a new technique to enable the creation of shape-bounded word cloud,we call ShapeWordle,in which we fit words to form a given shape.It satisfies the high filling rate while maintain the data fidelity in the word cloud.To guide word placement within a shape,we extend the traditional Archimedean spirals to be shape-aware by formulating the spirals in a differential form using the distance field of the shape.To handle non-convex shapes,we introduce a multi-centric Wordle layout method that segments the shape into parts for our shape-aware spirals to adaptively fill the space and generate word placements.We offer a set of editing interactions to facilitate the creation of semantically-meaningful Wordles.In addition,we combine our technique with time-varying text data to help user further explore the dataTo verify the reliability of our method,we present a comprehensive comparison of our results against the state-of-the-art technique.And,we also provide case studies with 14 users.The results show that our method is capable of producing compact Wordles with shapes highly similar to the given outlines.And,our results demonstrate the advantages of ShapeWordle over approaches such as WordArt.