A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data

Abstract In the big data era, it is practical and generalizable to use social media data for decision-making. Thus, determining how to effectively use this kind of data to support large-scale group decision-making (LSGDM) is a direction worth studying. In this paper, for the first time, the sentiment analysis of social media data is used to evaluate the quality of LSGDM and then to dynamically determine the weights of experts from the statistical perspective. The proposed method helps improve the objectivity, accuracy, and acceptability of LSGDM. A case application demonstrates the feasibility of this method, and the results of comparative analyses reveal its superior accuracy and stability to a certain extent.

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