• DocumentCode
    3074951
  • Title

    Opinion Extraction & Classification of Reviews from Web Documents

  • Author

    Shandilya, Shishir K. ; Jain, Suresh

  • Author_Institution
    Dept. of Comput. Eng., Devi Ahilya Univ., Indore
  • fYear
    2009
  • fDate
    6-7 March 2009
  • Firstpage
    924
  • Lastpage
    927
  • Abstract
    Automatic extraction of opinions on products from Web has been receiving interest increasingly. Such extracted knowledge helps to find out what other people think about the particular product or service. With the growing availability of resources like online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden growth in the area of opinion mining, which deals with the computational techniques for opinion extraction and understanding created an utmost need to understand and view the Web in a different prospect. In this paper, we demonstrate an opinion-mining framework that extracts the opinions and views of the consumers/customers, and analyze them to provide concrete market flow along with proven statistical data. The software uses classification, clustering and lingual knowledge-based opinion mining for providing these features.
  • Keywords
    Web sites; classification; data mining; document handling; information retrieval; Web document; clustering; information retrieval; knowledge extraction; lingual knowledge-based opinion mining; market flow; online review site; opinion extraction; personal blog; reviews classification; Availability; Blogs; Concrete; Data mining; Information analysis; Information retrieval; Information technology; Learning systems; Spatial databases; Web mining; Information Retrieval; Opinion Mining; Web Mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advance Computing Conference, 2009. IACC 2009. IEEE International
  • Conference_Location
    Patiala
  • Print_ISBN
    978-1-4244-2927-1
  • Electronic_ISBN
    978-1-4244-2928-8
  • Type

    conf

  • DOI
    10.1109/IADCC.2009.4809138
  • Filename
    4809138