How Much Do We Say? Using Informativeness of Negotiation Text Records for Early Prediction of Negotiation Outcomes
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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.
Keywords
Electronic negotiations Text data mining Machine learning Language patterns Early prediction of success or failurePreview
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