Blog mining for the fortune 500

James Geller, Sapankumar Parikh, Sriram Krishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 5th International Conference, MLDM 2007, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783540734987
StatePublished - 2007
Event5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007 - Leipzig, Germany
Duration: Jul 18 2007Jul 20 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4571 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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