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Journals / Journal of Artificial Intelligence and Data Science / 2023 / Cilt: 3 Sayı: 1

Performance Evaluation of a Pretrained BERT Model for Automatic Text Classification

Sercan ÇEPNİ
Amine Gonca TOPRAK
Aslı YATKINOĞLU
Öykü Berfin MERCAN
Şükrü OZAN
Pages
27–35
Publication Date
2023-06-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this way, the suitability of the texts, which is an important component in determining the quality of online advertising, within the industry will be tested automatically.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents a pre-trained BERT model application on texts that are extracted from website URLs automatically to classify texts according to the industry.
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Key Findings & Data:Then, the dataset is cleaned and labeled with the relevant industries among 42 different categories.
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Conclusion & Impact:In this way, the suitability of the texts, which is an important component in determining the quality of online advertising, within the industry will be tested automatically.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:digital marketing · ad text · natural language processing

Abstract

This study presents a pre-trained BERT model application on texts that are extracted from website URLs automatically to classify texts according to the industry. With the aim of doing so, the related dataset is first obtained from different kinds of websites by web scraping. Then, the dataset is cleaned and labeled with the relevant industries among 42 different categories. The pre-trained BERT model which was trained on 101.000 advertisement texts in one of our previous ad text classification studies is used to classify texts. Classification performance metrics are then used to evaluate the pre-trained BERT model on the test set and 0.98 average accuracy and 0.67 average F1 score for different 12 categories are obtained. The method can be used to test the compatibility of texts to be used in online advertising networks with the advertiser's industry. In this way, the suitability of the texts, which is an important component in determining the quality of online advertising, within the industry will be tested automatically.

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Cite This Article

ÇEPNİ, S., TOPRAK, A. G., YATKINOĞLU, A., MERCAN, Ö. B., OZAN, Ş. (2023). Performance Evaluation of a Pretrained BERT Model for Automatic Text Classification. Journal of Artificial Intelligence and Data Science, Cilt: 3 Sayı: 1, 27–35. https://ajindex.com/articles/948013

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