ANALYSIS OF TEXT INFORMATION USING TF-IDF VECTOR SPACE AND MACHINE LEARNING METHODS

Taras Batiuk

doi:10.36074/logos-04.04.2025.039

Інформаційні технології та системи

April 4, 2025; Paris, France: VIII Міжнародна науково-практична конференція «DÉBATS SCIENTIFIQUES ET ORIENTATIONS PROSPECTIVES DU DÉVELOPPEMENT SCIENTIFIQUE»

ANALYSIS OF TEXT INFORMATION USING TF-IDF VECTOR SPACE AND MACHINE LEARNING METHODS

Taras Batiuk ⁺⁻

DOI: https://doi.org/10.36074/logos-04.04.2025.039
Опубліковано: 11.05.2025

Анотація

In the era of digital technologies and large amounts of data, the analysis of textual and numerical data becomes extremely important. The growing volume of unstructured information, such as textual descriptions, along with the accumulation of structured numerical data, creates new opportunities for deep analysis and obtaining useful conclusions.

Посилання

Jaradat S., Nayak R., Paz A., Elhenawy M. Ensemble Learning with Pre-Trained Transformers for Crash Severity Classification: A Deep N.L.P. Approach. Algorithms. 2024. № 17 (7). P. 284–284.
Lin Y., Wang X., Yang J., Wang S. Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship. Applied Sciences. 2024. № 14 (11). P. 4661–4661.
Aldyaflah I. M., Zhao W., Yang S., Luo X. The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study. Information. 2024. № 15 (6). P. 302–302.
Zou Z., Ji X., Li Y. A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network Model and a Mixed Event Extractor. Information. 2024. № 15 (6). P. 303–303.

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Як цитувати

Batiuk , T. (2025). ANALYSIS OF TEXT INFORMATION USING TF-IDF VECTOR SPACE AND MACHINE LEARNING METHODS. Збірник наукових праць «ΛΌГOΣ», (April 4, 2025; Paris, France), 209–213. https://doi.org/10.36074/logos-04.04.2025.039

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Статті цього автора (авторів), які найбільше читають

[1] Jaradat S., Nayak R., Paz A., Elhenawy M. Ensemble Learning with Pre-Trained Transformers for Crash Severity Classification: A Deep N.L.P. Approach. Algorithms. 2024. № 17 (7). P. 284–284.

[2] Lin Y., Wang X., Yang J., Wang S. Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship. Applied Sciences. 2024. № 14 (11). P. 4661–4661.

[3] Aldyaflah I. M., Zhao W., Yang S., Luo X. The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study. Information. 2024. № 15 (6). P. 302–302.

[4] Zou Z., Ji X., Li Y. A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network Model and a Mixed Event Extractor. Information. 2024. № 15 (6). P. 303–303.