Forming Predictive Features of Tweets for Decision-Making Support

2022/01/06 by Bohdan M. Pavlyshenko, Pavlyshenko, Bohdan M. · 1 citation
Business, Management and Accounting · Computer Science · Engineering · #Advanced Research in Systems and Signal Processing #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information Systems and Technology Applications #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2201.02049

openalex publication_date 2022/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The article describes the approaches for forming different predictive features of tweet data sets and using them in the predictive analysis for decision-making support. The graph theory as well as frequent itemsets and association rules theory is used for forming and retrieving different features from these datasests. The use of these approaches makes it possible to reveal a semantic structure in tweets related to a specified entity. It is shown that quantitative characteristics of semantic frequent itemsets can be used in predictive regression models with specified target variables.

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