We present a natural language processing model that allows automatic classification and prediction of the user’s negotiation style during the interaction with virtual humans in a 3D game. We collected the sentences used in the interactions of the users with virtual artificial agents and their associated negotiation style as measured by ROCI-II test. We analyzed the documents containing the sentences for each style applying text mining techniques and found statistical differences among the styles in agreement with their theoretical definitions. Finally, we trained two machine learning classifiers on the two datasets using pre-trained Word2Vec embeddings.
CITATION STYLE
Pacella, D., Dell’Aquila, E., Marocco, D., & Furnell, S. (2017). Toward an automatic classification of negotiation styles using natural language processing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10498 LNAI, pp. 339–342). Springer Verlag. https://doi.org/10.1007/978-3-319-67401-8_43
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