Automatic Generated Tag Clouds

This paper proposes a formal model for discussing the construction of tag clouds, applicable both to human beings and computer programs. From this model we also to propose a methodology for automatic generation of tag clouds, which aim to achieve results similar to the ones produced by human beings. We also present a specific implementation and describe some of our initial results. 1. Introduction With the advancement of social software sites, like Delicious[1], Flickr[2], and the increasing incentive to use tags to describe various types of information objects, it became common to present these tags in a format known as " tag cloud�. According to Hassan and Herrero­Solana[3], a tag cloud is a visual list of tags arranged in a way to transmit information and meaning through the use of different font sizes, styles and colors, based on the their importance within the group in which it appears. The more popular a tag is, the larger is the font and therefore it is more prominent in the tag cloud in which it occurs. Therefore, tag clouds provide a summary or a semantic view of the concepts most important to represent an object [4]. Human beings build this semantic view by associating concepts that are understood from an observed object and the words that represent them, under their specific points of view. If these objects are documents, those words can be found in their contents or inferred from the understanding of these contents. In general, people can easily create words that represent concepts, since they have a reasonable knowledge of the language and of the world. They can also describe concepts as sentences, even if partial. Automatic systems, however, do not own this knowledge; therefore, they must infer knowledge exclusively from the words that compose the document or some other information, such as metadata or the tagging applied to similar documents. We should also point out that the relation between words and concepts is not bijective. A single word can represent more than one concept, what is known as polysemy. A " crane�, for example, can represent a bird or construction equipment. On the other hand, a concept can be represented by many words (or sequence of words), such as " film�, " movie�, " picture�, " motion­picture� and " flick�, what is known as synonymy. Also, there could be no word that singularly represents a concept, for instance, " blood …