1. A method of automated discourse analysis of interaction transcriptions, the method comprising; receiving, by at least one processor, a plurality of interaction transcriptions; for each respective interaction transcript of the plurality of interaction transcriptions: executing, by the at least one processor, a zoning process that segments utterances of the respective interaction transcript into a plurality of meaning units; classifying, by the at least one processor, the plurality of meaning units into dialog acts using a trained dialog act classifier, the dialog acts include at least the following categories: social, information, request, response and repetition, the repetition category being used to identify a repetition of a immediately previous dialog act when one party to a given interaction repeats a meaning unit previously spoken by another party to the given interaction, wherein the dialog act classifier is trained by at least preparing a training file in Attribute-Relation File Format (ARFF) for Waikato Environment for Knowledge Analysis (WEKA) in which each row in the training file includes data about a single respective meaning, unit, and then applying Naïves Bayes to build the classifier from the training file; and identifying, by the at least one processor, durations between the dialog acts; selecting, by the at least one processor, a subset of the plurality of interaction transcriptions based on the durations; extracting, by the at least one processor, syntactic patterns from the selected subset; and outing, by the at least one processor, the extracted patterns as a visual presentation on a graphical display.
1. A method of automated discourse analysis of interaction transcriptions, the method comprising; receiving, by at least one processor, a plurality of interaction transcriptions; for each respective interaction transcript of the plurality of interaction transcriptions: executing, by the at least one processor, a zoning process that segments utterances of the respective interaction transcript into a plurality of meaning units; classifying, by the at least one processor, the plurality of meaning units into dialog acts using a trained dialog act classifier, the dialog acts include at least the following categories: social, information, request, response and repetition, the repetition category being used to identify a repetition of a immediately previous dialog act when one party to a given interaction repeats a meaning unit previously spoken by another party to the given interaction, wherein the dialog act classifier is trained by at least preparing a training file in Attribute-Relation File Format (ARFF) for Waikato Environment for Knowledge Analysis (WEKA) in which each row in the training file includes data about a single respective meaning, unit, and then applying Naïves Bayes to build the classifier from the training file; and identifying, by the at least one processor, durations between the dialog acts; selecting, by the at least one processor, a subset of the plurality of interaction transcriptions based on the durations; extracting, by the at least one processor, syntactic patterns from the selected subset; and outing, by the at least one processor, the extracted patterns as a visual presentation on a graphical display. 10. The method of claim 1 , further comprising selecting, by the at least one processor, the subset that resulted in a cancellation outcome.