PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Performance of k-nearest neighbors algorithm in opinion classification

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents another approach for determining document’s semantic orientation process. It includes a brief introduction describing the area of application of opinion mining, and some definitions useful in the field. The most commonly used methods are mentioned and some alternative ones are described. Experiment results are presented which show that kNN algorithm gives similar results to proportional algorithm.
Słowa kluczowe
Rocznik
Strony
97--110
Opis fizyczny
Bibliog. 16 poz., tab., fig.
Twórcy
  • Poznań University of Technology, Institute of Computing Science, Piotrowo 2, 60-965 Poznań, Poland
autor
  • Poznań University of Technology, Institute of Computing Science, Piotrowo 2, 60-965 Poznań, Poland
Bibliografia
  • [1] Stop listy. http://pl.wikipedia.org/wiki/Stop_listy. Wikipedia, wolna encyklopedia, Accessed 12 February 2010.
  • [2] Białecki A., Stempel - Algorithmic Stemmer for Polish Language, http://www.getopt.org/stempel/, Accessed 12 February 2010.
  • [3] Dave K., Lawrence S., Pennock D.M., Mining the peanut gallery: opinion extraction and semantic classification of product reviews, in: Proceedings of the 12th international conference on World Wide Web, New York, USA, 2003, 519-528.
  • [4] Hatzivassiloglou V., McKeown K.R., Predicting the semantic orientation of adjectives, Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th Conference of the European, New Brunswick, Canada, 1997, 174-181.
  • [5] Horrigan J., Online shopping, Pew Internet & American Life Project Report, 2008.
  • [6] Hu M., Liu B., Mining opinion features in customer reviews, in: Proceedings of the19th national conference on Artificial intelligence, 2004, 755-760.
  • [7] Jędrzejewski K., Morzy M., Opinion Mining and Social Networks: a Promising Match, in: First Workshop on Social Network Analysis in Applications SNAA 2011. Kaohsiung, Taiwan, 2011.
  • [8] Pang B., Lee L., Opinion Mining and Sentiment Analysis, Now Publishers inc, 2008.
  • [9] Popescu A.M., Entzioni O., Extracting Product Features and Opinions from Reviews, in: Kao A., Poteet S.R. (Eds). Natural Language Processing and Text Mining, Springer, London, 2007, 9-28.
  • [10] Rainie L., Horrigan J., Election 2006 online, Pew Internet & American Life ProjectReport, 2007.
  • [11] Salton G., Wong A., Yang C.S., A vector space model for automatic indexing. Technical Report, New York, USA, 1974.
  • [12] Turney P.D., Littman M.L., Unsupervised learning of semantic orientation from a Hundred-Billion-word corpus, 2002.
  • [13] Wang G., Araki K., Modifying SO-PMI for Japanese Weblog Opinion Mining by Using a Balancing Factor and Detecting Neutral Expressions, in: Proceedings of NAACL HLT 2007, Companion Volume 2007, New York, USA, 2007, 189-192.
  • [14] Weiss D., Miłkowski M., Morfologik-stemming, http://morfologik.blogspot.com/, Accessed 12 February 2010.
  • [15] Xu R.F., Wong K.F., Xia Y.Q., Coarse-Fine Opinion Mining - WIA, in: NTCIR-7 MOATTask. In. Proceedings of NTCIR-7 Workshop, Japan, 2008.
  • [16] Zhang W., Yu C., Meng W., Opinion retrieval from blogs, in: Proceedings of thesixteenth ACM conference on Conference on information and knowledge management, 2007, 831-840.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9a776909-985c-478a-9974-9da1f273de29
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.