Antal Kristóf, Fekete and Erika, Baksáné Varga (2023) Comparison of text classification methods. PRODUCTION SYSTEMS AND INFORMATION ENGINEERING, 11 (1). pp. 16-28. ISSN 1785-1270
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Abstract
The paper presents a comparison of some text categorization methods in terms of accuracy and learning speed. These methods are selected specifically for large dataset, therefore only the Random Forest algorithm is considered from the numerous machine learning techniques. In addition to this, two LSTM models are studied as – based on our literature review – these are found best suited to the text classification task among neural networks. Our research goal is to find evidence for or against this statement. Therefore we build, train and test a classic multilayer perceptron model and show its accuracy and learning speed as compared to the other methods.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Text classification, Random Forest, Neural networks, LSTM, MLP |
| Subjects: | Q Science / természettudomány > Q1 Science (General) / természettudomány általában Q Science / természettudomány > QA Mathematics / matematika |
| Depositing User: | Anita Agárdi |
| Date Deposited: | 21 Nov 2025 18:20 |
| Last Modified: | 21 Nov 2025 18:20 |
| URI: | https://real.mtak.hu/id/eprint/229608 |
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