Negotiation Outcome Classification Using Language Features

Douglas P. Twitchell, Matthew L. Jensen, Douglas C. Derrick, Judee K. Burgoon, Jay F. Nunamaker

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

In this paper we discuss the relationships among negotiations, integrative and distributive speech acts, and classification of negotiation outcome. Our findings present how using automated linguistic analysis can show the trajectory of negotiations towards convergence (resolution) or divergence (non-resolution) and how these trajectories accurately classify negotiation outcomes. Consequently, we present the results of our negotiation outcome classification study, in which we use a corpus of 20 transcripts of actual face-to-face negotiations to build and test two classification models. The first model uses language features and speech acts to place negotiation utterances onto an integrative and distributive scale. The second uses that scale to classify the negotiations themselves as successful or unsuccessful at the midpoint, three-quarters of the way through, and at the end of the negotiation. Classification accuracy rates were 80, 75, and 85 % respectively.

Original languageEnglish (US)
Pages (from-to)135-151
Number of pages17
JournalGroup Decision and Negotiation
Volume22
Issue number1
DOIs
StatePublished - Jan 1 2013

Keywords

  • Classification
  • Language features
  • Machine learning
  • Negotiation
  • Negotiation outcome
  • Speech acts

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Arts and Humanities (miscellaneous)
  • Social Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

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