Ad Text Classification with Transformer-Based Natural Language Processing Methods
This work addresses the need for efficient ad text classification in online advertising, but it is incremental as it applies an existing method to a new language and dataset.
The study tackled the problem of automatically classifying ad texts from online advertising platforms into 12 sectors using a transformer-based NLP method, achieving classification efficiencies with a pre-trained BERT model for Turkish on a dataset of approximately 21,000 labeled texts.
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.