Abstract
Business negotiations represent a form of communication where informativeness, i.e., the amount of provided information, depends on context and situation. In this study, we hypothesize that relations exist between language signals of informativeness and the success or failure of negotiations. We support our hypothesis through linguistic and statistical analysis which acquires language patterns from records of electronic text-based negotiations. Empirical results of machine learning experiments show that the acquired patterns are useful for early prediction of negotiation outcomes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adair W, Brett JM (2005) The negotiation dance: time, culture, and behavioral sequences in negotiation. Org Sci 16(1): 33–51
Carston R (1998) Informativeness, relevance and scalar implicature. In: Carston R, Uchida S (eds) Relevance theory: applications and implications. John Benjamins, Amsterdam, pp 179–236
Chu-Carroll J, Carberry S (2000) Conflict resolution in collaborative planning dialogues. Int J Hum Comput Stud 53(6): 969–1015
Grice P (1989) Studies in the way of words. Harvard University Press, Cambridge, Massachusetts
Hall M, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6): 1437–1447
Hine M, Murphy S, Weber M, Kersten G (2009) The role of emotion and language in dyadic e-negotiations. Group Decis Negotiat 18: 193–211
Kamakura W, Basuroy S, Boatwright P (2006) Is silence golden? An inquiry into the meaning of silence in professional product evaluations. Quant Mark Econ 4: 119–141
Kersten G, Zhang G (2003) Mining Inspire data for the determinants of successful internet negotiations. Cent Eur J Oper Res 11(3): 297–316
Koeszegi S, Pesendorfer E-M, Vetschera R (2007) Data-driven episodic phase analysis of e-negotiation. Group Decis Negot 2007 2: 113–130
Leech G, Svartvik J (2002) A communicative grammar of English. Longman, London
Loken B (2006) Consumer psychology: categorization, inferences, affect, and persuasion. Ann Rev Psychol 57: 453–485
Mahmoudi MT, Badie K, Kharrat M (2008) Text organization via projection from researcher-space onto text-space. Kybernetes 37(8): 1151–1164
Manning C, Schutze H (2003) Foundations of statistical natural language processing. The MIT Press
Marriot H (1995) Intercultural business negotiation, the discourse of negotiation. In: Firth A (ed), The discourse of negotiation. Studies of languages in the workplace. Pergamon, pp 247–268
McKenna K, Green A, Gleason M (2002) Relationship formation on the internet: what’s the big attraction? J Soc. Issues 58(1): 9–31
Nastase V, Shirabad JS (2007) A study of sentiment and gender influence on negotiation outcome in electronic negotiations. In: Proceedings of group decision and negotiations, vol 2, pp 491–500
Oakes M (2003) Statistics for corpus linguistics. Edinburg University Press, Scotland
Pennebaker J, Francis M, Booth R (2001) Linguistic inquiry and word count: LIWC 2001. Erlbaum Publishers, Mahwah
Reitter D, Moore J (2007) Predicting success in dialogue. In: Proceedings of the 45th annual meeting of the association of computational linguistics (ACL 2007). Association for Computational Linguistics, pp 808–815
Roget’s Interactive Thesaurus (2006) http://thesaurus.reference.com/
Roloff M, Putnam L (1992) Introduction, communication and negotiation. In: Roloff M, Putnam L (eds) Communication and negotiation. Sage, London
Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1): 1–47
Simons T (1993) Speech patterns and the concept of utility in cognitive maps: the case of integrative bargaining. Acad Manag J 38(1): 139–156
Sokolova M (2006) Learning from communication data: language in electronic busines negotiations. Ph.D. dissertation
Sokolova M, Szpakowicz S (2006) Language patterns in the learning of strategies from negotiation texts. In: Proceedings of the 19th Canadian conference on artificial intelligence (AI’2006), pp 288–299, Springer
Sokolova M, Lapalme G (2007) Informativeness for prediction of negotiation outcomes. In: Proceedings of group decision and negotiations 2007, vol 2, pp 501–511
Sokolova M, Nastase V, Szpakowicz S (2008) The telling tail: signals of success in electronic negotiation texts. In: Proceedings of the third international joint conference on natural language processing (IJCNLP 2008) pp 257–264
Sperber S, Wilson D (2006) Pragmatics. In: Jackson F, Smith M (eds) Oxford handbook of contemporary philosophy. Oxford University Press, Oxford
Takeuchi H, Subramaniam L, Nasukawa T, Roy S (2007) Automatic identification of important segments and expressions for mining of business-oriented conversations at contact centers. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL) pp 458–467
Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los Altos, CA
Yu G (2010) Lexical diversity in writing and speaking task performances. Appl Linguist 31(2): 236–259
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sokolova, M., Lapalme, G. How Much Do We Say? Using Informativeness of Negotiation Text Records for Early Prediction of Negotiation Outcomes. Group Decis Negot 21, 363–379 (2012). https://doi.org/10.1007/s10726-010-9211-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10726-010-9211-9