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IA-Regional-Radio – Social Network for Radio Recommendation

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Book cover Computational Social Network Analysis

Part of the book series: Computer Communications and Networks ((CCN))

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Abstract

This chapter describes the functions of a system proposed for the music hit recommendation from social network data base. This system carries out the automatic collection, evaluation and rating of music reviewers and the possibility for listeners to rate musical hits and recommendations deduced from auditor’s profiles in the form of regional Internet radio. First, the system searches and retrieves probable music reviews from the Internet. Subsequently, the system carries out an evaluation and rating of those reviews. From this list of music hits, the system directly allows notation from our application. Finally, the system automatically creates the record list diffused each day depending on the region, the year season, the day hours and the age of listeners. Our system uses linguistics and statistic methods for classifying music opinions and data mining techniques for recommendation part needed for recorded list creation. The principal task is the creation of popular intelligent radio adaptive on auditor’s age and region – IA-Regional-Radio.

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References

  1. Beineke P, Hastie T, Vaithyanathan S (2004) Exploring sentiment summarization. In: Proceedings of the AAAI spring symposium on exploring attitude and affect in text, AAAI technical report SS-04-07, Barcelona, Spain

    Google Scholar 

  2. Cover TM, Thomas JA (1991) Elements of information theory. Wiley New York (ISBN 0-471-06259-6)

    Google Scholar 

  3. Dagan I, Karov Y, Roth D (1997) Mistakedriven learning in text categorization. In: 2nd conference on empirical methods in natural language processing, EMNLP-97, Providence, RI, USA, pp 55–63

    Google Scholar 

  4. Das S, Chen M (2001) Yahoo! for Amazon : Extracting market sentiment from stock message boards. In: Proceedings of the 8th Asia Pacific finance association annual conference (APFA), Bangkok, Thailand

    Google Scholar 

  5. Dave K, Lawrence S, M, Pennock D (2003) Mining the peanut gallery : Opinion extraction and semantic classification of product reviews. In: Proceedings of WWW, Budapest, Hungary, pp 519–528

    Google Scholar 

  6. Drucker H, Vapnik V, Wu D (1999) Automatic text categorization and its applications to text retrieval. IEEE Trans Neural Netw 10:1048–1054

    Article  Google Scholar 

  7. Dziczkowski G, Wegrzyn-Wolska K (2007) Rcss – Rating critics support system purpose built for movies recommendation. In: Advances in intelligent web mastering. Springer, Verlag France

    Google Scholar 

  8. Dziczkowski G, Wegrzyn-Wolska K (2008) An autonomous system designed for automatic detection and rating of film. Extraction and linguistic analysis of sentiments. In: Proceedings of IEEE/WIC/ACM inter conference on web intelligence and intelligent agent technology, Sydney, pp 847–850

    Google Scholar 

  9. Dziczkowski G, Wegrzyn-Wolska K (2008) Tool of the intelligence economic: Recognition function of reviews critics. In: ICSOFT 2008 proceedings. INSTICC Press, Setúbal Portugal, pp 218–223

    Google Scholar 

  10. Gross M (1997) The construction of local grammars. Finite-state languauge processing. The MIT Press, Cambridge, Mass, pp 329–354

    Google Scholar 

  11. Hatzivassiloglou V, McKeown K (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the of the joint ACL/EACL conference, Madrid, Spain, pp 174–181

    Google Scholar 

  12. Hatzivassiloglou V, Wiebe, J (2000) Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In: Proceedings of the international conference on computational linguistics (COLING), Universitat des Saarlandes, Saarbrucken, Germany

    Book  Google Scholar 

  13. Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. In: 10th European conference on machine learning, ECML-98, Chemnitz, Germany, pp 137–142

    Google Scholar 

  14. Joachims T (1999) Transductive inference for text classification using support vector machines. In: 16th international conference on machine learning, ICML-99, Bled, Slovenia, pp 200–209

    Google Scholar 

  15. Joachims T, Sebastiani F (2002) Guest editors introduction to the special issue on automated text categorization. J Intell Inform Syst, 18:103–105

    Article  Google Scholar 

  16. Knight K (1999) Mining online tex. Commun ACM 42(1):58–61

    Article  Google Scholar 

  17. Lewis DD, Gale WA (1994) A sequential algorithm for training text classifiers. In: 17th ACM international conference on research and development in information retrieval, SIGIR-94, Special Issue of the SIGIR Forum, Dublin, Ireland, pp 3–12

    Google Scholar 

  18. Lewis DD, Haues PJ (1994) Guest editorial for the special issue on text categorization. ACM Trans Inform Syst

    Google Scholar 

  19. Mediametrie (2008)www.mediametrie.fr∕new.php?rubrique = rad&newsid = 229

    Google Scholar 

  20. Mitchell TM (1996) Machine learning. McGraw Hill

    Google Scholar 

  21. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), University of Pennsylvania, Philadelphia, USA, pp 79–86

    Google Scholar 

  22. Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the association for computational linguistics (ACL), Barcelona, Spain, pp 271–278

    Google Scholar 

  23. Pazienza MT (1997) Information extraction. In: Lecture notes in computer science, vol 1299, Springer, Frascati, Italy

    Google Scholar 

  24. Porter WA (1980) Synthesis of polynomic systems. J-SIAM-J-MATH-ANA 11:308–315

    Article  MATH  Google Scholar 

  25. Schmid H (1994) Part-of-speech tagging with neural network. In: 15th conference on computational linguistics, Kyoto, Japan, 1:172–176

    Google Scholar 

  26. Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Article  MATH  MathSciNet  Google Scholar 

  27. Turney P (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the association for computational linguistics (ACL), pp 417–424

    Google Scholar 

  28. Voorhess EM (1999) Natural language processing and information retrieval. Information extraction, toward scalable, adaptable systems, Lecture notes in computer science, pp 32–48

    Google Scholar 

  29. Wang Y, Hodges J, Tang B (2005) Classification of web documents using naive bayes method. In: Proceedings of the 15th IEEE international conference on tools with artificial intelligence, pp 560–564

    Google Scholar 

  30. Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of the ACM SIGIR conference on information and knowledge management (CIKM), Bremen, Germany, pp 625–631

    Google Scholar 

  31. Wiebe JM, Wilson T, Bruce R, Bell M, Martin M (2004) Learning subjective language. Computat Linguist 30(3):277–308

    Article  Google Scholar 

  32. Wiebe J, Mihalcea R (2006) Word sense and subjectivity. In: Proceedings of the 21st conference on computational linguistics/association for computational linguistics (COLING/ACL), Sydney, Australia, pp 1065–1072

    Google Scholar 

  33. Wilson T, Wiebe J, Hwa R (2006) Just how mad are you? Finding strong and weak opinion clauses. In: Proceedings of the AAAI, pp 761–769. Ext. version in Computat Intell 22(2 Special Issue on Sentiment Analysis):73–99

    Google Scholar 

  34. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the human language technology conference and the conference on empirical methods in natural language processing (HLT/EMNLP), pp 347–354

    Google Scholar 

  35. Yang Y, Liu X (1999) A re-examination of text categorization methods. In: 22nd ACM international conference on research and development in information retrieval, SIGIR-99, Berkeley, CA, USA, pp 42–49

    Google Scholar 

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Author information

Authors and Affiliations

  1. Departement Informatique et Automatique 941, Ecole des Mines de Douai, rue Charles Bourseul, BP 10838, 59-500, Doaui, France

    Grzegorz Dziczkowski

  2. Ecole Superieur d’Ingenieurs en Informatique et Genie des Telecommunication, 77-215, Avon-Fontainebleau Cedex, France

    Lamine Bougueroua & Katarzyna Wegrzyn-Wolska

Authors
  1. Grzegorz Dziczkowski
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  2. Lamine Bougueroua
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  3. Katarzyna Wegrzyn-Wolska
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Corresponding author

Correspondence to Grzegorz Dziczkowski .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation &, Machine Intelligence Research Labs (MIR), Auburn, 98071-2259, U.S.A.

    Ajith Abraham

  2. Fac. Computers & Information, Cairo University, Ahmed Zewal Street 5, Giza, 12613, Egypt

    Aboul-Ella Hassanien

  3. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, Ostrava, 708 33, Czech Republic

    Vaclav Sná¿el

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Dziczkowski, G., Bougueroua, L., Wegrzyn-Wolska, K. (2010). IA-Regional-Radio – Social Network for Radio Recommendation. In: Abraham, A., Hassanien, AE., Sná¿el, V. (eds) Computational Social Network Analysis. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-229-0_16

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  • DOI: https://doi.org/10.1007/978-1-84882-229-0_16

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-228-3

  • Online ISBN: 978-1-84882-229-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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