Comparative Analysis of Text Data in Successful Face-to-Face and Electronic Negotiations

Marina Sokolova (), Mohak Shah () and Stan Szpakowicz ()
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Marina Sokolova: University of Ottawa
Mohak Shah: University of Ottawa
Stan Szpakowicz: University of Ottawa

Group Decision and Negotiation, 2006, vol. 15, issue 2, No 3, 127-140

Abstract: Abstract Various combination of Natural Language Processing and Machine Learning methods offer ample opportunities wherever texts are an important element of an application or a research area. Such methods discover patterns and regularities in the data, seek generalization and in effect learn new knowledge. We have employed such methods in learning from a large amount of textual data. Our application is electronic negotiations. The genre of texts found in electronic negotiations may seem limited. It is an important research question whether our methods and findings apply equally well to texts that come from face-to-face negotiations. In order to confirm such more general applicability, we have analyzed comparable collections of texts from electronic and face-to-face negotiations. We present our findings on the extent of similarity between these two related but distinct genres. In this study we have analyzed similarities in the text data of electronic and face-to-face negotiations. The results show that – in certain conditions – vocabulary richness, language complexity and text predictability are similar.

Keywords: Electronic negotiations; face-to-face negotiations; communication process; text data; corpus analysis (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10726-006-9024-z

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