Forming Predictive Features of Tweets for Decision-Making Support

TL;DR

This paper presents methods for extracting predictive features from tweet data using graph theory and association rules, enabling semantic analysis and improving decision-making models.

Contribution

It introduces a novel approach combining graph theory and frequent itemsets for semantic feature extraction from tweets for predictive analysis.

Findings

Semantic structure can be revealed in tweets using these methods.

Quantitative features from frequent itemsets improve regression models.

Approaches support decision-making with tweet data.

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.