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Forming Predictive Features of Tweets for Decision-Making Support

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arxiv 2201.02049 v1 pith:CWW4DB7F submitted 2022-01-06 cs.CL cs.AIcs.IRcs.LG

Bohdan M. Pavlyshenko This is my paper
classification cs.CLcs.AIcs.IRcs.LG
keywords predictivefeaturesformingapproachesdecision-makingdifferentfrequentitemsets
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model

    cs.CL 2025-08 reject novelty 3.0 of 10

    A fine-tuned Mistral 7B model produces graph and text summaries, sentiment scores, and stacked meta-summaries of crypto news, but the paper reports no quantitative evaluation.

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