Abstract
In recent years there has been a tremendous increase in the number of users maintaining online blogs on the Internet. Companies, in particular, have become aware of this medium of communication and have taken a keen interest in what is being said about them through such personal blogs. This has given rise to a new field of research directed towards mining useful information from a large amount of unformatted data present in online blogs and online forums. We discuss an implementation of such a blog mining application. The application is broadly divided into two parts, the indexing process and the search module. Blogs pertaining to different organizations are fetched from a particular blog domain on the Internet. After analyzing the textual content of these blogs they are assigned a sentiment rating. Specific data from such blogs along with their sentiment ratings are then indexed on the physical hard drive. The search module searches through these indexes at run time for the input organization name and produces a list of blogs conveying both positive and negative sentiments about the organization.
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Geller, J., Parikh, S., Krishnan, S. (2007). Blog Mining for the Fortune 500. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_29
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DOI: https://doi.org/10.1007/978-3-540-73499-4_29
Publisher Name: Springer, Berlin, Heidelberg
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