Customer Interaction Management Goes Social: Getting Business Processes Plugged in Social Networks



Within this chapter, we will describe a novel technical service dealing with the integration of social networking channels into existing business processes. Since many businesses are moving to online communities as a means of communicating directly with their customers, social media has to be explored as an additional communication channel between individuals and companies. While the English-speaking consumers on Facebook are more likely to respond to communication rather than to initiate communication with an organisation, some German companies already have regularly updated Facebook pages for customer service and support, e.g. Telekom. Therefore, the idea of classifying and evaluating public comments addressed to German companies is based on an existing demand. In order to maintain an active Facebook wall, the consumer posts have to be categorised and then automatically assigned to the corresponding business processes (e.g. the technical service, shipping, marketing, accounting, etc.). This service works like an issue tracking system sending e-mails to the corresponding person in charge of customer service and support. That way, business process management systems which are already used to e-mail communication can benefit from social media. This allows the company to follow general trends in customer opinions on the Internet; moreover it facilates the recording of two-sided communication for customer relationship management and the company’s response will be delivered through consumer’s preferred medium: Facebook.


Business Process Social Networking Site Customer Service Customer Relationship Management Contact Centre 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We express our sincere thanks to the German Federal Ministry of Economics and Technology for financing this research within the collaborative research project SocialCom. Our prototype was created in cooperation with the Munich-based German company Telenet GmbH Kommunikationssysteme.


  1. 1.
    Appelt, D.E., Israel, D.J.: Introduction to information extraction technology. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm (1999)Google Scholar
  2. 2.
    Appelt, D., Hobbs, J., Bear, J., Israel, D., Tyson, M.: FASTUS: a finite-state processor for information extraction from real-world text. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), Chambéry, pp. 1172–1178 (1993)Google Scholar
  3. 3.
    Bennett, K.P., Campbell, C.: Support vector machines: hype or hallelujah? SIGKDD Explor. Newsl. 2, 1–13 (2000)CrossRefGoogle Scholar
  4. 4.
    Browne, R., Clements, E., Harris, R., Baxter, S.: Business and consumer communication via online social networks: a preliminary investigation. In: Australian and New Zealand Marketing Academy (ANZMAC) Conference, Melbourne (2009)Google Scholar
  5. 5.
    Bsiri, S., Geierhos, M., Ringlstetter, C.: Structuring job search via local grammars. Adv. Nat. Lang. Process. Appl. Res. Comput. Sci. (RCS) 33, 201–212 (2008)Google Scholar
  6. 6.
    Courtois, B.: Dictionnaires électroniques DELAF anglais et français. In: Leclère, C., Laporte, E., Piot, M., Silberztein, M. (eds.) Lexique, Syntaxe et Lexique-Grammaire; Syntax, Lexis & Lexicon-Grammar, pp. 113–123. John Benjamins, Amsterdam/Philadelphia (2004)Google Scholar
  7. 7.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: a framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, University of Philadelphia (2002)Google Scholar
  8. 8.
    Feldman, R., Rosenfeld, B., Fresko, M.: Teg – a hybrid approach to information extraction. Knowl. Inf. Syst. 9(1), 1–18 (2006)CrossRefGoogle Scholar
  9. 9.
    Friburger, N., Maurel, D.: Finite-state transducer cascades to extract named entities in texts. Theor. Comput. Sci. 313, 93–104 (2004)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Grishman, R.: Adaptive information extraction and sublanguage analysis. In: Proceedings of Workshop on Adaptive Text Extraction and Mining at Seventeenth International Joint Conference on Artificial Intelligence, Seattle (2001)Google Scholar
  11. 11.
    Gross, M.: Local grammars and their representation by finite automata. In: Hoey, M. (ed.) Data, Description, Discourse, Papers on the English Language in Honour of John McH Sinclair, pp. 26–38. Harper-Collins, London (1993)Google Scholar
  12. 12.
    Gross, M.: The construction of local grammars. In: Roche, E., Schabes, Y. (eds.) Finite-State Language Processing, pp. 329–354. MIT Press, Cambridge (1997)Google Scholar
  13. 13.
    Gross, M.: A bootstrap method for constructing local grammars. In: Contemporary Mathematics: Proceedings of the Symposium, University of Belgrad, Belgrad, pp. 229–250 (1999)Google Scholar
  14. 14.
    Guenthner, F.: Electronic Lexica and Corpora Research at CIS. Int. J. Corpus Linguist. 1(2), 287–301 (1996)CrossRefGoogle Scholar
  15. 15.
    Harris, Z.S.: Mathematical structures of language. Intersci. Tracts Pure Appl. Math. 21, 152–156 (1968)Google Scholar
  16. 16.
    Harris, Z.S.: Language and information. Bampton Lect. Am. 28, 33–56 (1988)Google Scholar
  17. 17.
    Hunston, S., Sinclair, J.: A local grammar of evaluation. In: Hunston, S., Thompson, G. (eds.) Evaluation in Text: Authorial Stance and the Construction of Discourse, pp. 74–101. Oxford University Press, Oxford (2000)Google Scholar
  18. 18.
    Jacobs, P.S., Rau, L.F.: The GE NLToolset: a software foundation for intelligent text processing. In: Proceedings of the 13th International Conference on Computational Linguistics, Helsinki, pp. 373–375 (1990)Google Scholar
  19. 19.
    Jacobs, P.S., Krupka, G., Rau, L., Mauldin, M., Mitamura, T., Kitani, T., Sider, I., Childs, L.: GE-CMU: Description of the SHOGUN system used for MUC-5. In: Proceedings of the Fifth Message Understanding Conference, Baltimore, pp. 109–120 (2001)Google Scholar
  20. 20.
    Lee, Y.S.: Website-Klassifikation und Informationsextraktion aus Informationsseiten einer Firmenwebsite. Ph.D. thesis, Ludwig-Maximilians-Universität München (2008)Google Scholar
  21. 21.
    Maurel, D., Guenthner, F.: Automata and Dictionaries. Texts in Computing Science, vol. 6. King’s College Publications, London (2006)Google Scholar
  22. 22.
    McDonald, D.: Internal and external evidence in the identification and semantic categorization of proper names. In: Boguraev, B., Pustejovsky, J. (eds.) Corpus Processing for Lexical Acquisition, pp. 21–39. MIT Press, Cambridge (1996)Google Scholar
  23. 23.
    Mikheev, A., Moens, M., Grover, C.: Named entity recognition without gazetteers. In: Proceedings of the Ninth Conference of the European Chapter of the Association for Computational Linguistics, Bergen, pp. 1–8 (1999)Google Scholar
  24. 24.
    Patwardhan, S., Riloff, E.: Learning domain-specific information extraction patterns from the web. In: IEBeyondDoc ’06: Proceedings of the Workshop on Information Extraction Beyond the Document, pp. 66–73. Association for Computational Linguistics, Morristown (2006)Google Scholar
  25. 25.
    Paumier, S.: Unitex user manual 2.1 (2010).
  26. 26.
    Poibeau, T.: Extraction d’information: du texte brut au web sémantique. Hermès, Paris (2003)Google Scholar
  27. 27.
    Rennie, J.D.M., Teevan, J., Karger, D.R.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning, Washington, pp. 616–623 (2003)Google Scholar
  28. 28.
    Riloff, E., Jones, R.: Learning dictionaries for information extraction by multi-level bootstrapping. In: Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-1999), Orlando, pp. 474–479 (1999)Google Scholar
  29. 29.
    Rish, I.: An empirical study of the Naive Bayes classifier. In: IJCAI-01 Workshop on “Empirical Methods in AI” (2001)Google Scholar
  30. 30.
    Steinwart, I., Christmann, A.: Support Vector Machines. Information Science and Statistics. Springer, New York (2008)MATHGoogle Scholar
  31. 31.
    Woods, W.A.: Transition network grammars for natural language analysis. Commun. ACM. 13(10), 591–606 (1970)MATHCrossRefGoogle Scholar
  32. 32.
    Yangarber, R., Grishman, R.: NYU: description of the proteus/PET system as used for MUC-7 ST. In: Proceedings of the Seventh Message Understanding Conference (1998)Google Scholar
  33. 33.
    Yoon, D., Choi, S.M., Sohn, D.: Building customer relationships in an electronic age: the role of interactivity of e-commerce web sites. Psychol. Mark. 25, 602–618 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.CIS, Centre for Information and Language ProcessingLudwig-Maximilians-Universität MünchenMünchenGermany
  2. 2.Institute for the Protection and Security of the CitizenIPSC, European Commission, Joint Research Centre – Ispra siteIspraItaly

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