论文标题
构成推文的预测特征以进行决策支持
Forming Predictive Features of Tweets for Decision-Making Support
论文作者
论文摘要
本文介绍了形成推文数据集不同预测特征的方法，并在预测分析中使用它们来进行决策支持。图理论以及频繁的项目集和关联规则理论用于形成和检索这些数据股的不同特征。这些方法的使用使得可以在与指定实体有关的推文中揭示语义结构。结果表明，语义频繁项目集的定量特性可用于具有指定目标变量的预测回归模型。
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.
