Language Patterns in the Learning of Strategies from Negotiation Texts

The paper shows how to construct language patterns that signal influence strategies and tactical moves corresponding to such strategies. We apply corpus analysis methods to the extraction of certain multiword patterns from the text data of electronic negotiations. The patterns thus acquired become features in the task of classifying those texts. A series of machine learning experiments predicts the negotiation outcome from the texts associated with first halves of negotiations. We compare the results with the classification of complete negotiations.