Performance of K-Nearest Neighbors Algorithm in Opinion Classification

Open access

Abstract

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.

References
  • [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 DM, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, in: Proceedings of the 12th internationalconference on World Wide Web, New York, USA, 2003, 519-528.

  • [4] Hatzivassiloglou V, McKeown KR, Predicting the semantic orientation of adjectives, Proceedings of the 35th Annual Meeting of the Association for ComputationalLinguistics 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 AM, Entzioni O, Extracting Product Features and Opinions from Reviews, in: Kao A, Poteet SR (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 CS, A vector space model for automatic indexing. Technical Report, New York, USA, 1974.

  • [12] Turney PD, Littman ML, 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 ofNAACL 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 RF, Wong KF, Xia YQ, 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 knowledgemanagement, 2007, 831-840.

Foundations of Computing and Decision Sciences

The Journal of Poznan University of Technology

Journal Information


CiteScore 2016: 0.75

SCImago Journal Rank (SJR) 2016: 0.330
Source Normalized Impact per Paper (SNIP) 2016: 0.709

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