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Website category classification using fine-tuned BERT language model

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Date
2020
Author
Demirkıran, Ferhat
Çayır, Aykut
Ünal, Uğur
Dağ, Hasan
Abstract
The contents on the Word Wide Web is expanding every second providing web users a rich content. However, this situation may cause web users harm rather than good due to its harmful or misleading information. The harmful contents can contain text, audio, video, or image that can be about violence, adult contents, or any other harmful information. Especially young people may readily be affected with these harmful information psychologically. To prevent youth from these harmful contents, various web filtering techniques, such as keyword filtering, Uniform Resource Locator (URL) based filtering, Intelligent analysis, and semantic analysis, are used. We propose an algorithm that can classify websites, which may contain adult contents, with 67.81% (BERT) accuracy among 32 unique categories. We also show that a BERT model gives higher accuracy than both the Sequential and Functional API models when used for text classification.

Source

5th International Conference on Computer Science and Engineering, UBMK 2020

Pages

333-336

URI

https://doi.org/10.1109/UBMK50275.2020.9219384
https://hdl.handle.net/20.500.12469/3562

Collections

  • Araştırma Çıktıları / Scopus [1482]
  • Yönetim Bilişim Sistemleri / Management Information Systems [141]

Keywords

BERT
Functional API
Sequential API
Text classification
Web filtering

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Contact Us | Send Feedback
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