A method for reducing the impact of information risks on a megaproject life cycle based on a semantic information field

This paper consider a comprehensive method for devising loyalty programs based on the stages of a life cycle of an international megaproject. The method is based on the analysis of information risks and their management. The method is focused on aggregation and processing of data from various sources of textual information, which demonstrates the attitudes of key categories of individuals regarding the implementation of a megaproject at its numerous life cycle stages. The semantic information field is formed using hardware and software based on Neural Network Technologies. Authors examine the most popular neural network architectures that are used in the sentiment analysis. The paper describes a comparative analysis of classification accuracy of neural network architectures based on volume of texts and neural network profitability to sentiment analysis of large and small volumes of text. The method is aimed at managing and influencing information flow that accompanies the implementation of a megaproject stages. The application of semantic information field makes it possible to account for the informational risks of a megaproject and to prepare an effective set of measures to counteract these risks in a timely manner. This work was supported by RFBR grant № 20-010-00708\20.