2021/06/21 by Umut Özdil, Özdil, Umut, Büşra Arslan +9
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2106.10899
6 pages, in Turkish language, 4 figures, 3 tables
openalex publication_date 2021/06/21 · arxiv created 2021/06/23 · arxiv updated 2021/06/24 · openalex created_date 2021/07/05 · openalex updated_date 2026/07/28
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.