形成用于决策支持的推文的预测特征
Forming Predictive Features of Tweets for Decision-Making Support
摘要：
本文描述了形成不同的预测特征的tweet数据集的方法，并将其用于决策支持的预测分析。利用图论、频繁项集和关联规则理论，从这些特征中形成和提取不同的特征。这些方法的使用使得揭示与指定实体相关的推文中的语义结构成为可能。结果表明，语义频繁项集的数量特征可以用于具有特定目标变量的预测回归模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
