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Forming Predictive Features of Tweets for Decision-Making Support

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 77))

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 datasets. 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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Author information

Authors and Affiliations

  1. Ivan Franko National University of Lviv, Lviv, Ukraine

    Bohdan M. Pavlyshenko

Authors
  1. Bohdan M. Pavlyshenko
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Editor information

Editors and Affiliations

  1. Department of Physics, Kherson State University, Kherson, Ukraine

    Sergii Babichev

  2. Department of Informatics and Computer Science, Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Pavlyshenko, B.M. (2022). Forming Predictive Features of Tweets for Decision-Making Support. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_32

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