Reorganizing clouds: A study on tag clustering and evaluation

Highlights? Representing tags by co-occurrences yields more accurate clusters than representing them by content. ? Representations based on co-occurrences significantly reduce the computational cost of the process. ? Some of the studied functions to weight co-occurrences outperform approaches used in earlier works. ? Our dataset presents a reliable solution to build approaches for finding tag relations while evaluating on sound quantitative criteria. ? Language modeling techniques help discover that the usage of some tags is biased to unexpected meanings. Finding and visualizing semantic relations among tags within a tag cloud enhances user experience, particularly regarding access to and retrieval of web pages on social tagging systems. Several approaches have been proposed to visualize tag relations in these systems. However, results of previous research rely on qualitative evaluation methods, and do not provide robust and sound comparison criteria. In order to allow quantitative evaluation we present a benchmark social tagging dataset, where a subset of 140 tags from a well-known social bookmarking site, delicious, have been manually categorized according to the open directory project (ODP). The manual categorization is utilized as a ground truth that enables quantitative evaluation providing a way of inferring the best of different clustering approaches. With this dataset we also explore different tag representation approaches to present a reorganized tag cloud by using self organizing maps. In addition, we present an approach to enrich the resultant tag cloud with the most characteristic terms for each tag and group of tags, making possible a further filtered navigation, both by tag and document content, and easing a deeper qualitative evaluation of the clusters.

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