ECIS 2020 Research Papers

Abstract

Business negotiations – be they face-to-face or electronic – are conducted through communication enabling the declaration of negotiation objectives and active implementation of negotiation strategies to achieve pre-defined goals and the declaration of a successful or unsuccessful end of the negotiation. The processing of exchanged textual communication enables the automatic transformation of unstructured data into processable structured datasets and subsequently the analysis of textual content without losing the data richness of exchanged communication messages. For this purpose, the paper presents Text Mining-based pre-processing approaches and dimensionality reduction algorithms from Feature Extraction and Feature Selection in a research framework and evaluates those to counteract common dimensionality problems with textual processing. In doing so, the maintenance of data richness in communication data is considered as the overall goal to determine the dataset with minimal information loss. In this sense, various pre-processed and transformed communication datasets derived from dimensionality reduction are integrated as input data into selected classification models to measure the prediction performance regarding the final negotiation outcome with ROC analysis. The central results of the ROC show that quantified business communication generated by Optimized Selection delivers the best data based on Lovins’ stemming algorithm compared to stemming variations of Forward Selection and SVD.

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