JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Binary text classification using machine learning and transformer models for small and imbalanced datasets
Abstract
Aims: The increasing speed with which platforms providing digital content have entered our lives has made the classification of the content on these platforms important. In this study, the binary classification problem of Netflix content as a movie or show was addressed, the effect of metadata features and text descriptions on an accurate classification process was examined and a new hybrid approach was presented.
Methods: Two Netflix datasets from open sources (Kaggle and Hugging-Face) were used in the study. Furthermore, so as to eliminate the imbalance in the datasets, machine learning (ML) and deep learning (DL) models with Natural Language Processing (NLP) were used separately and hybridly on the two datasets downloaded for shows and movies, both individually and combined. In this way, the dataset was examined separately as imbalanced and balanced, and the imbalance was eliminated by merging two datasets.
Results: The proposed hybrid framework achieved the results of 0,970940 accuracy, 0,982801 precision, 0,936768 recall and 0,959233 F1-score.
Conclusion: It has been demonstrated through experimental results that the obtained results are better than those of other models. In the experiments, various machine learning methods and transformer based language models were evaluated; it was observed that the metadata-based models showed high performance in terms of accuracy, whereas BERT and similar models were particularly successful in detecting the ‘show’ class. The study reveals that the structured metadata and textual data are complementary in nature, and it is shown that hybrid approaches combining machine learning with transformer models would produce more balanced and robust results.

Keywords : Text classification, natural language processing, ensemble learning, hybrid learning, ELECTRA

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Article available in :
Volume 4, Issue 2, 2026
Page : 83-91
https://doi.org/10.51271/JCEEES-0041
Article Information
Received : 14 Haz 2026
Accepted : 22 Tem 2026
Published : 13 Eki 2026
Corresponding Author :

İsmail Şenyurt: Gazi University, Ankara, Turkiye

[email protected]
Citation : Şenyurt, İ., & Toklu, S. (2026). Binary text classification using machine learning and transformer models for small and imbalanced datasets. J Comp Electr Electron Eng Sci, 4(2), 83-91. https://doi.org/10.51271/JCEEES-0041

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