Keskin, Enes FarukAcikgoz, ErkutDogan, Gulustan2024-07-052024-07-05202397983503456502835-371410.1109/ICCRE57112.2023.101555872-s2.0-85166183027https://doi.org/10.1109/ICCRE57112.2023.10155587https://hdl.handle.net/20.500.14411/2552In this paper, the studies carried out to detect objectionable expressions in any text will be explained. Experiments were performed with Sentence transformers, supervised machine learning algorithms, and Bert transformer architecture trained in English, and the results were observed. To prepare the dataset used in the experiments, the natural language processing and machine learning methodologies of the toxic and non-toxic contents in the labeled text data obtained from the Kaggle platform are explained, and then the methods and performances of the models trained using this dataset are summarized in this paper.eninfo:eu-repo/semantics/closedAccesslanguage modelsberttransformersnatural language processingsupervised machine learning algorithmsdeep learningConference Object1620WOS:0010214963000040