Social tagging is the process of assigning and sharing among users freely selected terms of resources. This approach enables users to annotate/ describe resources, and also allows users to locate new resources through the collective intelligence of other users. Social tagging offers a new avenue for resource discovery as compared to taxonomies and subject directories created by experts. This chapter investigates the effectiveness of tags as resource descriptors and is achieved using text categorization via support vector machines (SVM). Two text categorization experiments were done for this research, and tags and Web pages from del.icio. us were used. The first study concentrated on the use of terms as its features while the second used both terms and its tags as part of its feature set. The experiments yielded a macroaveraged precision, recall, and F-measure scores of 52.66%, 54.86%, and 52.05%, respectively. In terms of microaveraged values, the experiments obtained 64.76% for precision, 54.40% for recall, and 59.14% for F-measure. The results suggest that the tags were not always reliable indicators of the resource contents. At the same time, the results from the terms-only experiment were better compared to the experiment with both terms and tags. Implications of our work and opportunities for future work are also discussed.
The Use of Support Vector Machines When Designing a User-Defined Niche Search Engine
