Affiliations: [a] Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia | [b] IRISA, Université de Rennes 1, Rennes cedex 35042, France | [c] Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI-10000 Ljubljana, Slovenia | [d] Université Lyon, ERIC-Lyon2, 5 av. Pierre Mendès-France, 69676 Bron Cedex, France
Abstract: Explorative data analysis in text mining essentially relies on effective visualization techniques which can expose hidden relationships among documents and reveal correspondence between documents and their features. In text mining, the documents are most often represented by feature vectors of very high dimensions, requiring dimensionality reduction to obtain visual projections in two- or three-dimensional space. Correspondence analysis is an unsupervised approach that allows for construction of low-dimensional projection space with simultaneous placement of both documents and features, making it ideal for explorative analysis in text mining. Its present use, however, has been limited to word-based features. In this paper, we investigate how this particular document representation compares to the representation with letter n-grams and word n-grams, and find that these alternative representations yield better results in separating documents of different class. We perform our experimental analysis on a bilingual Croatian-English parallel corpus, allowing us to additionally explore the impact of features in different languages on the quality of visualizations.
Keywords: Text mining, Text visualization, Letter n-grams, Word n-grams, Correspondence analysis
