A Game-Theoretic Lexical Link Analysis for Discovering High-Value Information from Big Data

We demonstrate a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover high-value information from big data. In this paper, high-value information refers to the information that has the potential to grow its value over time. LLA is a unsupervised learning method that does not require manually labeled training data. New value metrics are defined based on a game-theoretic framework for LLA. In this paper, we show the value metrics generated from LLA in a use case of analyzing business news. We show the results from LLA are validated and correlated with the ground truth. We show that by using game theory, the high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information, and at the same time generates high social welfare, therefore, contains higher intrinsic value.