A uniform theoretic approach to opinion and information retrieval (Contributo in volume (capitolo o saggio))

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Label
  • A uniform theoretic approach to opinion and information retrieval (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Alternative label
  • G. Amati; G. Amodeo; M. Bianchi; C. Gaibisso; G. Gambosi (2010)
    A uniform theoretic approach to opinion and information retrieval
    Springer, Berlin (Germania) in Intelligent Information Access, 2010
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • G. Amati; G. Amodeo; M. Bianchi; C. Gaibisso; G. Gambosi (literal)
Pagina inizio
  • 83 (literal)
Pagina fine
  • 108 (literal)
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  • Intelligent Information Access (literal)
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  • 301 (literal)
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  • 26 (literal)
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  • Giambattista Amati: Fondazione Ugo Bordoni, Rome, Italy Giuseppe Amodeo: Dept. of Computer Science, University of L'Aquila, Italy Marco Bianchi: Istituto di Analisi dei Sistemi ed Informatica \"Antonio Ruberti\" - CNR, Rome, Italy Carlo Gaibisso: Istituto di Analisi dei Sistemi ed Informatica \"Antonio Ruberti\" - CNR, Rome, Italy Giorgio Gambosi: Dept. of Mathematics, University of Rome \"Tor Vergata\", Italy (literal)
Titolo
  • A uniform theoretic approach to opinion and information retrieval (literal)
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  • Studies in Computational Intelligence, Intelligent Information Access (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-13999-4 (literal)
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  • Armano, Giuliano; De Gemmis, Marco; Semeraro, Giovanni; Vargiu, Eloisa (literal)
Abstract
  • In this paper, we introduce a supervised method for the generation of a dictionary of weighted opinion bearing terms from a collection of opinionated documents. We also describe how such a dictionary is used in the framework of an algorithm for opinion retrieval, that is for the problem of identifying the documents in a collection where some opinion is expressed with respect to a given query topic. Several experiments, performed on the TREC Blog collection, are reported together with their results; in these experiments, the use of different combinations of DFR (Divergence from Randomness) probabilistic models to assign weights to terms in the dictionary and to documents is studied and evaluated. The results show the stability of the method and its practical utility. Moreover, we investigate the composition of the generated lexicons, mainly focusing on the presence of stop-words. Quite surprisingly, the best performing dictionaries show a predominant presence of stop-words. Finally, we study the effectiveness of the same approach to generate dictionaries of polarity-bearing terms: preliminary results are provided. (literal)
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