Dimensionality reduction for blog tag mining
Flora S. Tsai · International Journal of Web Engineering and Technology · 2011
Blog tags are labels of blog documents that classify them into different categories. Most tags are user-generated, which create problems such as inconsistencies in tags across different users, blogs without tags, lack of descriptive tags, lack of semantic distinction, etc. In this paper, we utilise dimensionality reduction techniques to reduce the inherent noise in blog tags. A tag-topic model is combined with dimensionality reduction, and then evaluated on real-world blog data. By employing dimensionality reduction techniques to reduce the document-tag space, better classification results were achieved. This indicates that the noise in tags can be effectively reduced by representing the original set of tags with a smaller number of latent tags, which can lead to more accurate real-time categorisation of blog documents.