BloSEn: Blog Search Engine Based On Post Concept Clustering
This is an incremental improvement for blog search users, focusing on popular platforms like Blogger and Wordpress.
The paper tackles the problem of improving blog search by incorporating concept clustering based on semantic content analysis, achieving a user relevance cumulative metric of 95.44% in experiments.
This paper focuses on building a blog search engine which doesn't focus only on keyword search but includes extended search capabilities. It also incorporates the blog-post concept clustering which is based on the category extracted from the blog post semantic content analysis. The proposed approach is titled as "BloSen (Blog Search Engine)". It involves in extracting the posts from blogs and parsing them to extract the blog elements and store them as fields in a document format. Inverted index is being built on the fields of the documents. Search is induced on the index and requested query is processed based on the documents so far made from blog posts. It currently focuses on Blogger and Wordpress hosted blogs since both these hosting services are the most popular ones in the blogosphere. The proposed BloSen model is experimented with a prototype implementation and the results of the experiments with the user's relevance cumulative metric value of 95.44% confirms the efficiency of the proposed model.