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Strategies and language trends in learning success and failure of negotiation

Abstract

In negotiation by electronic means, language is an important deal-making tool which helps realize negotiation strategies. Negotiators may use language to request information, exchange offers, persuade, threaten, as well as reach a compromise or find prospective partners. All this is recorded in texts exchanged by negotiators. We explore the language signals of strategies—argumentation, persuasion, negation, proposition. Leech and Svartvik’s approach to language in communication gives our study the necessary systematic background. It combines pragmatics, the communicative grammar and the meaning of English verbs. Language signals become features in the task of classifying those texts. We employ Statistical Natural Language Processing and Machine Learning techniques to find general trends that negotiation texts exhibit. Our hypothesis is that language signals help predict negotiation outcomes. We run experiments on the Inspire data. The electronic negotiation support system Inspire was gathering data for several years. The data include text messages which negotiators may exchange while trading offers. We conduct a series of Machine Learning experiments to predict the negotiation outcome from the texts associated with first halves of negotiations. We compare the results with the classification of complete negotiations. We conclude the paper with an analysis of the results and a list of suggestions for future work.

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References

  • Alpaydin E (2004) Introduction to machine learning. MIT Press

  • Brett JM (2001) Negotiating globally. Jossey-Bass, San Francisco

    Google Scholar 

  • Cellich C, Jain SC (2004) Global business negotiations: a practical guide. Thomson, South-Western

  • Cherkassky V, Muller F (1998) Learning from data. Wiley

  • Cowie R, Douglas-Cowie E, Apolloni B, Taylor J, Romano A, Fellenz W (1999). What a neural net need to know about emotion words. In: Mastorakis N (ed) Computational intelligence and applications (CSCC ’99), pp 109–114

  • Devillers L, Lamel L, Vasilescu I (2003) Emotion detection in task-oriented spoken dialogs. In: Proc of IEEE internation conference multimedia and expo, Baltimore, pp 549–552

  • Donohue WA, Ramesh CN (1992) Negotiator–opponent relationships. In: Putnam L, Roloff M (eds) Communication and negotiation. Sage, London, pp 209–232

    Google Scholar 

  • Galley M, McKeown K, Hirschberg J, Shriberg E (2004) Identifying agreement and disagreement in conversational speech: use of bayesian networks to model pragmatic dependencies. In: Proceedings of ACL’2004, pp 669–676

  • Gebauer J, Scharl A (1999) Between flexibility and automation: an evaluation of web technology from a business process perspective. J Comp-Med Commun 5(2). http://www.ascusc.org/jcmc/vol5/issue2/gebauer.htm.

  • Gries ST (2003) Multifactorial analysis in corpus linguistics. Continuum, New York

  • Hargie O, Dickson D (2004) Skilled interpersonal communication: research, theory and practice, 4th edn. Routledge

  • Johannesson NL (1976) The english modal auxiliaries: a stratificational account. Acta Universitatis Stockholmiensis,

  • Jurafsky D, Martin JH (2000) Speech and language processing. Prentice Hall

  • Kersten GE(2003) The science and engineering of e-negotiation: an introduction. In: Proceedings of 36th Hawaii international conference on system sciences (HICSS – 2003), pp 27–36

  • Kersten GE et al (2002–2006) Electronic negotiations, media and transactions for socio-economic interactions, http://www.interneg.org/enegotiation

  • Kersten GE, Zhang G (2003) Mining inspire data for the determinants of successful internet negotiations. Central Eur J Operat Res 11(3):297–316

    Google Scholar 

  • Koeszegi S, Pesendorfer E-M, Srnka K(2006) Electronic negotiations a comparison of different support systems. Die Betriebswirtschaft 64(4):44–463

    Google Scholar 

  • Roloff M (ed), Putnam L (1992) Communication and negotiation. Sage, London

  • Leech G, Svartvik J (2002) A Communicative Grammar of English. Longman

  • Leech GN (1983) Principles of pragmatics. Longman

  • Leech GN (2004) Meaning and the English Verb. Longman

  • Koper R, Burrell N (1998) The efficacy of powerful/powerless language on attitudes and source credibility. In: Allen M, Preiss R (eds) Persuasion: advances through meta-analysis. Hampton Press, pp 203–215

  • Nastase V (2005) Concession curve for inspire data. Group Decis Negot 15(2):185–193

    Article  Google Scholar 

  • Nasukawa T, Yi J (2003) Sentiment analysis: Capturing favorability using natural language processing. In: Proc ICKC, pp 70–77

  • Nigam K, Hurst M (2004) Towards a robust metric of opinion. In: AAAI Spring Symposium on Exploring Attitude and Affect in Text

  • Perkins MR (1983) Modal expressions in english. Ablex Publishing Corporation

  • Schoop M (2003) A language-action approach to electronic negotiations. In: Proc 8th international working conference on the language-action perspective on communication modelling (LAP 2003), pp 143–160

  • Sebastiani F (2002) Machine learning in automate text categoriazation. ACM Comput Surv 34(1):1–47

    Article  Google Scholar 

  • Sjostedt G (ed) (2003) Professional cultures in international negotiations. Lexington Books

  • Sokolova M, Nastase V, Szpakowicz S (2004)Language in electronic negotiations: patterns in completed and uncompleted negotiations. In: Natural language processing (Proceedings of 3rd international conference on natural language processing (ICON ’2004)), pp 142–151

  • Sokolova M, Nastase V, Szpakowicz S, Shah M (2005) Analysis and models of language in electronic negotiations. In: Draminski M, Grzegorzewski P, Trojanowski K, Zadrozny S (eds) Issues in intelligent systems. Models and Techniques, EXIT, Warszawa, pp 197–211

  • Sokolova M, Szpakowicz S (2005) Analysis and classification of strategies in electronic negotiations. In: Advances in artificial intelligence (Proceedings of 18th conference of the canadian society for computational studies of intelligence (AI ’05)), pp 145–157

  • Srnka K, Koeszegi S (2007) From words to numbers—how to transform rich qualitative data into meaningful quantitative results: guidelines and exemplary study. Schmalenbach’s Bus Rev 59:29–57

    Google Scholar 

  • Ströbel M (2000) Effects of electronic markets on negotiation process. In: Proceedings of the 8th European conference on information systems, Vienna, vol 1, pp 445–452

  • Thompson LL (2005) The mind and heart of the negotiator, 3rd edn. Pearson Prentice Hall

  • Tottie G (1991) Negation in english speech and writing. Academic Press Inc.

  • Warren B (1984) Classifying adjectives. Acta Universitatis Gothoburgensis

  • Witten I, Frank E (2005) Data Mining Morgan Kaufmann, http://www.cs.waikato.ac.nz/ml/weka

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Acknowledgements

This work has been supported by a project on electronic negotiations, part of the Initiative on the New Economy of the Social Sciences and Humanities Research Council of Canada. Support also came from the Natural Sciences and Engineering Research Council of Canada. For the Inspire data we thank the InterNeg team led by Gregory Kersten.

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Correspondence to Stan Szpakowicz.

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Sokolova, M., Szpakowicz, S. Strategies and language trends in learning success and failure of negotiation. Group Decis Negot 16, 469–484 (2007). https://doi.org/10.1007/s10726-007-9083-9

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Keywords

  • Electronic negotiations
  • Written communication
  • Influence strategies
  • Language patterns
  • Corpus linguistics
  • Machine learning