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Fusion Approach to Finding Opinionated Blogs Public Deposited

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  • In this paper, we describe a fusion approach to finding opinionated blog postings. Our approach to opinion blog retrieval consisted of first applying traditional IR methods to retrieve on-topic blogs and then boosting the ranks of opinionated blogs based on combined opinion scores generated by multiple assessment methods. Our opinion module is composed of the Opinion Term Module, which identifies opinions based on the frequency of opinion terms (i.e., terms that occur frequently in opinion blogs), the Rare Term Module, which uses uncommon/rare terms (e.g., “sooo good”) for opinion classification, the IU Module, which uses IU (I and you) collocations, and the Adjective-Verb Module, which uses computational linguistics’ distribution similarity approach to learn the subjective language from training data.
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  • Yang, K., Yu, N., Valerio, A., Zhang, H., & Ke, W. (2007). Fusion approach to finding opinionated blogs. Proceedings of the American Society for Information Science and Technology, 44(1), 1-14.
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  • description.provenance : Approved for entry into archive by Michael Boock(michael.boock@oregonstate.edu) on 2016-04-13T21:47:28Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) Yang_et_al-2007-Proceedings_of_the_American_Society_for_Information_Science_and_Technology.pdf: 691906 bytes, checksum: 867d4266b5c6103b6a798f63b5942bff (MD5)
  • description.provenance : Made available in DSpace on 2016-04-13T21:47:28Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) Yang_et_al-2007-Proceedings_of_the_American_Society_for_Information_Science_and_Technology.pdf: 691906 bytes, checksum: 867d4266b5c6103b6a798f63b5942bff (MD5) Previous issue date: 2007
  • description.provenance : Submitted by Hui Zhang (hui.zhang@oregonstate.edu) on 2016-04-13T20:15:18Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) Yang_et_al-2007-Proceedings_of_the_American_Society_for_Information_Science_and_Technology.pdf: 691906 bytes, checksum: 867d4266b5c6103b6a798f63b5942bff (MD5)

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