Forming Predictive Features of Tweets for Decision-Making Support
This work addresses decision-making support for users analyzing tweet data, but it appears incremental as it applies existing methods to a specific domain.
The paper tackles the problem of extracting predictive features from tweet datasets to support decision-making by using graph theory and frequent itemsets to reveal semantic structures, and shows that quantitative characteristics of these itemsets can be incorporated into regression models with target variables.
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.