Trend detection using NLP as a mechanism of decision support

The purpose of the article is to present the principles of the developed algorithm for identifying trends based on the analysis of big text data and presenting the result in formats that are convenient for decision makers, implemented in the iFORA Big Data Mining System. The paper provides an overview of existing text analytics algorithms; outlines the mathematical basis for identifying terms that mean trends, which is proposed and tested on dozens of implemented projects; describes approaches to clustering terms based on their vectors in the Word2vec space; provides examples of two key visualizations (semantic, trend maps), which outline the range of topics and trends that characterize a particular area of study, as a way to adapt the results of the analysis to the tasks of decision makers. The limitations and advantages of using the proposed approach for decision support are discussed, and directions for future research are suggested.