A Study on the Role of Similarity Measures in Visual Text Analytics

Text Analytics is essential for a large number of applications and good approaches to obtain visual mappings of text are paramount. Many visualization techniques, such as similarity based point placement layouts, have proved useful to support visual analysis of documents. However, they are sensitive to data quality, which, in turn, relies on a critical preprocessing step that involves text ‘cleaning’ and in some cases term detecting and weighting, as well as the definition of a similarity function. There has been limited discussion on the effect of these important similarity calculations in the quality of visual representations. In this work we studied the effect of different text similarity measurements on the quality of visual text mappings. We focused mainly on two types of distance functions, those based on the well-known text vector representation and on direct string comparison measurements, comparing their effect on visual mappings obtained with point placement techniques. We find that both have their value but, in many circumstances, the recently introduced incremental vector space model (iVSM) is the best solution when discrimination is important. Based on the results of our evaluation we offer recommendations on the application of different text similarity measurements for Visual Text Analytics tasks. Keywords-Visual Text Analytics, Visual Text Mining, Vector Space Model, High-dimensional Data Visualization, Multidimensional Projections.