Ad Text Classification with Transformer-Based Natural Language Processing Methods
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Abstract (arXiv)
In this study, a natural language processing-based (NLP-based) method is proposed for the sector-wise automatic classification of ad texts created on online advertising platforms. Our data set consists of approximately 21,000 labeled advertising texts from 12 different sectors. In the study, the Bidirectional Encoder Representations from Transformers (BERT) model, which is a transformer-based language model that is recently used in fields such as text classification in the natural language processing literature, was used. The classification efficiencies obtained using a pre-trained BERT model for the Turkish language are shown in detail.
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References · 6
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
- 2018Aspect Based Sentiment Analysis with Gated Convolutional Networkscited by 65
- 2020Comparing BERT against traditional machine learning text classificationcited by 45
- 2019Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classificationcited by 22
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