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Sentiment-oriented contextual advertising

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Abstract

Web advertising (Online advertising), a form of advertising that uses the World Wide Web to attract customers, has become one of the world’s most important marketing channels. This paper addresses the mechanism of Content-based advertising (Contextual advertising), which refers to the assignment of relevant ads to a generic web page, e.g., a blog post. As blogs become a platform for expressing personal opinion, they naturally contain various kinds of expressions, including both facts and comments of both a positive and negative nature. Besides, in line with the major tenet of Web 2.0 (i.e., user-centric), we believe that the web-site owners would be willing to be in charge of the ads which are positively related to their contents. Hence, in this paper, we propose the utilization of sentiment detection to improve Web-based contextual advertising. The proposed sentiment-oriented contextual advertising (SOCA) framework aims to combine contextual advertising matching with sentiment analysis to select ads that are related to the positive (and neutral) aspects of a blog and rank them according to their relevance. We experimentally validate our approach using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method can effectively identify those ads that are positively correlated with the given blog pages.

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References

  1. Anagnostopoulous A, Broder AZ, Gabrilovich E, Josifovski V, Riedei L (2007) Just-in-time contextual advertising. In: Proceedings of the 16th ACM conference on CIKM, pp 331–340

  2. Bollegala D, Matsuo Y, Ishizuka M (2007) An integrated approach to measuring semantic similarity between words using information available on the Web. In: Proceedings of the NAACL HLT, pp 340–347

  3. Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval, Chap 8. Addison Wesley, Reading

    Google Scholar 

  4. Boztug Y, Reutterer T (2006) A combined approach for segment-specific analysis of market basket data. Eur J Oper Res

  5. Broder A, Fontoura M, Josifovski V, Riedel L (2007) A semantic approach to contextual advertising. In: Proceedings of the 30th international conference on SIGIR, pp 559–566

  6. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machine

  7. Chatterjee P, Hoffman DL, Novak TP (2003) Modeling the clickstream: implications for web-based advertising efforts. Mark Sci 22: 520–541

    Article  Google Scholar 

  8. Esuli A, Sebastiani F (2006) SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th conference on language resources and evaluation, pp 417–422

  9. Feng J, Bhargava HK, Pennock D (2003) Comparison of allocation rules for paid placement advertising in search engine. In: Proceedings of the 5th international conference on electronic commerce, pp 294–299

  10. Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions. Statistical methods for rates and proportions, Chap 6. Wiley, New York

  11. Gupta N, Yang F, Demers AJ, Gehrke J, Shanmugasundaram J (2007) User-centric personalized extensibility for data-driven web applications. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data, pp 1125–1127

  12. Hatzivassiloglou V, Mckeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the 35th ACL, pp 174–181

  13. Hu M, Liu B (2004) Mining opinion features in customer reviews. In: Proceedings of the conference on AAAI, pp 755–760

  14. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the conference on SIGKDD, pp 168–177

  15. Jiang T, Tuzhilin A (2008) Dynamic micro-targeting: fitness-based approach to predicting individual preferences. Knowl Inf Syst Int J (KAIS). doi:10.1007/s10115-10008-10149-z

  16. Kim S-M, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on COLING, pp 1367–1373

  17. Kim S-M, Hovy E (2006) Automatic identification of Pro and Con reasons in online reviews. In: Proceedings of the COLING/ACL, pp 483–490

  18. Lacerda A, Cristo M, Gonçalves MA, Fan Wg, Ziviani N, Ribeiro-Neto B (2006) Learning to advertise. In: Proceedings of the 29th annual international conference on SIGIR, pp 549–556

  19. Langheinrich M, Nakamura A, Abe N, Kamba T, Koseki Y (1999) Unintrusive customization techniques for Web advertising. Int J Comp Telecomm Netw 31: 1259–1272

    Google Scholar 

  20. Mayora O, Daras P, Panebarco M, Achilleopoulos N, Stollenmayer P, Williams D, Magnenat-Thalmann N, Guerrero C, Pelt M, McGrath T, Fuenmayor E, Salama D, Alvarez F, Kalapanidas E, Shani A, Moine J-YL (2008) User centric media in the future internet trends and challenges. In: Proceedings of the 3rd international conference on Digital interactive media in entertainment and arts, pp 441–446

  21. Novak TP, Hoffman DL (1997) New metrics for new media: toward the development of Web measurement standards. World Wide Web J 2: 213–246

    Google Scholar 

  22. OneUpWeb. How keyword length affects conversion rates, January 2005. Available at http://www.oneupweb.com/landing/keywordstudy_landing.htm

  23. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on EMNLP, pp 79–86

  24. Papadopoulos S, Menemenis F, Kompatsiaris Y, Brato, B (2009) Lexical graphs for improved contextual Ad recommendation. In: Proceedings of the 31st European conference on information retrieval, pp 216–227

  25. Parsons J, Gallagher K, Foster KD (2000) Messages in the medium: an experimental investigation of web advertising effectiveness and attitudes toward web content. Hawaii International Conference on System Sciences

  26. Porter MF (1980) An algorithm for suffix stripping. Program 14(3): 130–137

    Google Scholar 

  27. Reutterer T, Mild A, Natter M, Taudes A (2006) A dynamic segmentation approach for targeting and customizing direct marketing campaigns. J Interact Mark 20: 43–57

    Article  Google Scholar 

  28. Ribeiro-Neto B, Cristo M, Golgher PB, Moura ESd (2005) Impedance coupling in content-targeted advertising. In: Proceedings of the 28th annual international conference on SIGIR, pp 496–503

  29. Riloff E, Wiebe J (2003) Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 conference on EMNLP, pp 105–112

  30. Sahami M, Heilman TD (2006) A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th international conference WWW, pp 377–386

  31. Sato S (2001) Automated editing of hypertext resume from the world wide web. In: Proceedings of the symposium on applications and the Internet, pp 15–22

  32. Sato S, Sasaki Y (2003) Automatic collection of related terms from the web. In: Proceedings of the 41st ACL, pp 121–124

  33. Tran T (2009) Protecting buying agents in e-marketplaces by direct experience trust modelling. Knowl Inf Syst Int J (KAIS). doi:10.1007/s10115-10008-10188-10115

  34. Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th ACL, pp 417–424

  35. Turney PD (2001) Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In: Proceedings of the ECML, pp 491–502

  36. Wang C, Zhang P, Choi R, D’Eredita M (2002) Understanding consumers attitude toward advertising. In: Proceedings of the 8th Americas conference on information systems, pp 1143–1148

  37. Wang P, Hu J, Zeng H-J, Chen Z (2009) Using Wikipedia knowledge to improve text classification. Knowl Inf Sys J (KAIS). doi:10.1007/s10115-10008-10152-10114

  38. Weideman M, Haig-Smith T (2002) An investigation into search engines as a form of targeted advert delivery. In: Proceedings of the 2002 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology, pp 258–258

  39. Wiebe J, Riloff E (2005) Creating subjective and objective sentence classifiers from unannotated texts. In: Proceeding of CICLing, international conference on intelligent text processing and computational linguistics, vol 3406 of LNCS. Springer, Heidelberg, pp 475–486

  40. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on HLT/ EMNLP, pp 347–354

  41. Wong W, W Liu MB (2006) Featureless similarities for terms clustering using tree-traversing ants. In: Proceedings of the 2006 international symposium on Practical cognitive agents and robots, pp 177–191

  42. Woon WL, Madnick2 S (2009) Asymmetric information distances for automated taxonomy construction. Knowl Inf Syst Int J (KAIS). doi:10.1007/s10115-10009-10203-10115

  43. Yu H, Hatzivassiloglou V (2003) Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 conference on EMNLP, pp 129–136

  44. Zang W, Yu C, Meng W (2007) Opinion retrieval from blogs. In: Proceedings of the 16th ACM conference on CIKM, pp 831–840

  45. Zhuang L, Jing F, Zhu X (2006) Movie review mining and summarization. In: Proceedings of the 15th ACM international conference on Information and knowledge management, pp 43–50

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  1. Department of Computer Science, National Central University, No. 300, Jung-Da Rd., Chung-Li, Tao-Yuan, 320, Taiwan, ROC

    Teng-Kai Fan & Chia-Hui Chang

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  1. Teng-Kai Fan
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  2. Chia-Hui Chang
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Correspondence to Chia-Hui Chang.

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Fan, TK., Chang, CH. Sentiment-oriented contextual advertising. Knowl Inf Syst 23, 321–344 (2010). https://doi.org/10.1007/s10115-009-0222-2

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