Sentence based semantic similarity measure for blog-posts
This addresses the difficulty of knowledge discovery in blog data for researchers and analysts, but it is incremental as it adapts existing similarity measures to a specific domain.
The paper tackled the problem of measuring semantic similarity between blog-posts, which are challenging due to their small size and relaxed grammar, by proposing a novel sentence-oriented algorithm. The result was applied to cluster political blogs and identify influential bloggers in Pakistan's blogosphere.
Blogs-Online digital diary like application on web 2.0 has opened new and easy way to voice opinion, thoughts, and like-dislike of every Internet user to the World. Blogosphere has no doubt the largest user-generated content repository full of knowledge. The potential of this knowledge is still to be explored. Knowledge discovery from this new genre is quite difficult and challenging as it is totally different from other popular genre of web-applications like World Wide Web (WWW). Blog-posts unlike web documents are small in size, thus lack in context and contain relaxed grammatical structures. Hence, standard text similarity measure fails to provide good results. In this paper, specialized requirements for comparing a pair of blog-posts is thoroughly investigated. Based on this we proposed a novel algorithm for sentence oriented semantic similarity measure of a pair of blog-posts. We applied this algorithm on a subset of political blogosphere of Pakistan, to cluster the blogs on different issues of political realm and to identify the influential bloggers.