A MODEL TO EXTRACT SENTIMENTAL KNOWLEDGE IN A SEMANTIC WEB CONTEXT

Year 2012, Volume: 7 Issue: 1, 5 - 19, 01.06.2012

Domenico Consolı

Abstract

Nowadays, in a social and semantic context, the web contains millions of data, generic information
and opinions. The semantic techonology, using specific ontologies, is very important to represent the web
knowledge and to understand the meaning of online textual information. To extract useful knowledge and
sentiment (positivity or negativity) of opinions it is necessary to have a model that interpreters this information
from both semantic and sentiment point of view. In this paper, after the analysis of the web semantic
architecture, we describe a framework of sentiment analysis to interpret and extract the sentiment of opinions
expressed by people about a product/service, public administrator or law.  

Keywords

MODEL, EXTRACT, SENTIMENTAL

References

• Boley H., Tabet S. and Wagner G. (2001). “Design Rationale of RuleML: A Markup Language for Semantic Web Rules”. In Proc. Semantic Web Working Symposium (SWWS’01), Stanford University, July/August 2001, pp. 381-401
• Boucouvalas A. and Zhe X. (2002). “Text-to emotion engine for real time internet communication”, in Proceedings of International Symposium on CSNDSP. UK: Staffordshire University, 2002, pp. 164-168
• Brickley D., Guha R.V. (2003). “RDF Vocabulary Description Language 1.0 : RDF Schema”,W3C Working Draft 23 January 2003 available online at http://www.w3.org/TR/rdf-schema/
• Consoli D., Diamantini C. and Potena D. (2008). "Improving the customer intelligence with Customer enterprise Customer model". In Proc. of the 10th International Conference on Enterprise Information Systems, Software Agents and Internet Computing (SAIC), Barcelona, Spain, June 12-16 2008, pp. 323- 326.
• Consoli D., Diamantini C., Potena D. (2009a). "Knowledge Mining from web customer opinions to improve enterprise product". In Proceedings 2th International Conference on Mathematics and Informatics, Bacau, September 8-10 2009, Vol. 19 (2009), n. 2, pp. 163-178
• Consoli D., Diamantini C., Potena D. (2009b). "Affective algorithm to polarize customer opinions" In Proc. of the 11th International Conference on Enterprise Information Systems, Human-Computer Interaction (HCI), Milan, Italy, May 6-10 2009, pp. 157-160.
• Dean M. and Schreiber G. (2003). "Web Ontology Language (OWL) Reference Version 1.0", W3C Working Draft, 21 Febbraio 2003 available online at http://www.w3.org/TR/owl-ref/
• Ekman P. (2007).“Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life” NY: OWL Books, 2007.
• Eiter T., Ianni G., Krennwallner T. and Axel Polleres (2008). “Rules and Ontologies for the Semantic Web”. In Reasoning Web, Cristina Baroglio, Piero A. Bonatti, Jan Maski, Massimo Marchiori, Axel Polleres, and Sebastian Schaffert (Eds.). Lecture Notes In Computer Science, Vol. 5224. Springer-Verlag, Berlin, Heidelberg, pp. 1-53
• Elliott C. (1992). “Knowledge acquisition within the affective reasoner” in Notes for the AAAI Spring Symposium: Cognitive Aspects of Knoweledge Acquisition, 1992, pp. 64-72 Esuli A. and Sebastiani F. (2006). “SentiWordNet: A publicly available lexical resource for opinion mining” in Proceedings of LREC-06, 5th Conference on Language Resources and Evaluation, Genova, IT, 2006, pp. 417422
• Flouris G., Manakanatas D., Kondylakis H., Plexousakis D. and Antoniou G. (2008). “Ontology change: Classification and survey”. Knowl. Eng. Rev. 23, 2 (June 2008), pp. 117-152
• Gerber A. J., Barnard A. and van der Merwe A. J.(2007). “Towards a semantic web layered architecture”. In Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering (SE'07), W. Hasselbring (Ed.). ACTA Press, Anaheim, CA, USA, pp. 353-362
• Gerber A., van der Merwe A. and Barnard A. (2008). “A functional semantic web architecture”. In Proceedings of the 5th European semantic web conference on The semantic web: research and applications (ESWC'08), Sean Bechhofer, Manfred Hauswirth, Hoffmann J., and Manolis Koubarakis (Eds.). Springer-Verlag, Berlin, Heidelberg, pp. 273-287.
• Gomez-Perez A. and Corcho O. (2002). “Ontology Specification Languages for the Semantic Web”. IEEE Intelligent Systems 17, 1 (January 2002), pp. 54-60
• Herman I (2010), “Short introduction to the Semantic Web”, avaiable online at: http://www.w3.org/People/Ivan/CorePresentations/IntroThroughExample/Slides.pdf
• Khong Chua W.W. and Soong Goh A.E. (2010). “Techniques for discovering correspondences between ontologies”. Int. J. Web Grid Serv. 6, 3 (September 2010), pp. 213-243
• Kuhlins S. and Korthaus A. (2003). “A multithreaded java framework for information extraction in the context of enterprise application integration” in ISICT ’03: Proceedings of the 1st international symposium on Information and communication technologies. Trinity College Dublin, 2003, pp. 518–523.
• Lyman P., Varian H.R., Charles P., Good N., Jordan L.L. and Pal J. (2003) “How much information?”, available online at http://www2.sims.berkeley.edu/research/projects/how-much-info-2003
• Mastrogiannis N., Boutsinas B. and Giannikos I. (2009). “A method for improving the accuracy of data mining classification algorithms”, Comput. Oper. Res. 36, 10 (Oct. 2009), pp. 2829-2839
• McGuinness D.L. and van Harmelen F. (2003) "Web Ontology Language (OWL): Overview", W3C Working Draft, 10 Febbraio available online at http://www.w3.org/TR/owl-features/
• Ortony C.-G. and A. Collins (1990) “The cognitive structure of emotions”, New York: Cambridge University Press, 1990
• Pennebaker J. W., M. E. Francis, and R. J. Booth (2001). “Linguistic inquiry and word count: Liwc” in Computer Software, Mahwah,NJ, 2001.
• S. Bolasco, A. Canzonetti, F. M. Capo, F. della Ratta-Rinaldi and B. K. Singh (2005). “Understanding text mining: A pragmatic approach” in Knowledge Mining, ser. Studies in Fuzziness and Soft Computing, S. Sirmakessis ed., Springer Verlag, 2005, vol. 185, pp. 31–50.
• Strapparava C. and Valitutti A. (2004). “Wordnet-affect: An affective extension of wordnet” in The 4th International Conference On Language Resources and Evaluation, 2004, pp. 1083-1086
• Strobbe M., Van Laere O., Dauwe S., Dhoedt B., De Turck F., Demeester P., van Nimwegen C., and Vanattenhoven J. (2010). “Interest based selection of user generated content for rich communication services”. J. Netw. Comput. Appl. 33, 2 (Mar. 2010), pp. 84-97
• Sumi K. (2008). “Anime de Blog: animation CGM for content distribution”. In Proceedings of the 2008 international Conference on Advances in Computer Entertainment Technology (Yokohama, Japan, December 03 - 05, 2008). ACE '08, vol. 352. ACM, New York, NY, 187-190.
• Tanawongsuwan P. (2010). “Part-of-Speech Approach to Evaluation of Textbook Reviews”, in Proceedings of the 2010 Second international Conference on Computer and Network Technology (April 23 - 25, 2010). ICCNT. IEEE Computer Society, Washington, DC, pp. 352-356
• Tim Berners-Lee, James Hendler and Ora Lassila (2001). “The Semantic Web. A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities”, Scientific American Magazine, May 17, 2001
• Valitutti A., Strapparava C., and Stock O. (2004). “Developing affective lexical resources” vol. 2, n. 1, pp. 61-83