 Dimensionality reduction framework for blog mining and visualisationFlora S. Tsai International Journal of Data Mining, Modelling and Management, 2012, vol. 4, issue 3, 267-285 Abstract: The growing abundance of blogs and new forms of social media has created a critical need for new technologies to transfer the digital realm of social media into a manageable form. Blog mining addresses the domain-specific problem of mining information from blog data. Although mining blogs may share many similarities to web and text documents, existing data mining techniques need to be reevaluated and adapted for the multidimensional representation of blog data, which exhibit dimensions not present in traditional documents. In this paper, a new approach is presented for blog mining and visualisation based on dimensionality reduction techniques. The author-topic model based on latent Dirichlet allocation was extended for analysing and visualising blog authors, links, and time. A framework based on dimensionality reduction is proposed to visualise the blog dimensions of content, tags, authors, links, and time. This framework has been successfully designed, implemented, and evaluated on real-world blog data. Keywords: blog mining; dimensionality reduction; visualisation; multidimensional scaling; MDS; isometric feature mapping; Isomap; locally linear embedding; LLE; latent Dirichlet allocation; LDA; blogs; blogging; weblogs; data mining; blog data. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:4:y:2012:i:3:p:267-285 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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