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Object-Oriented Approach for Ethnic Enmity Detection in Text Messages by NLP
(2024) Molchanova, M.; Mazurets, O.; Sobko, O.; Boiarchuk, I.; Мазурець, Олександр Вікторович
The effectiveness of the method was studied using the developed software by comparing the obtained answers with the validation set, and the trained FastForest machine learning model was evaluated using the metrics MicroAccuracy, MacroAccuracy, LogLoss, ConfusionMatrix, f1-measure, and Recall. Without changing the working training set, the metrics values were as follows: MicroAccuracy 0.9890, MacroAccuracy 0.9889, and LogLoss 0.0463. It was developed a software implementation of the method for detecting manifestations of ethnic hatred in text messages of social Internet networks by NLP tools, which uses natural language processing techniques and converts input data in the form of a trained FastForest classifier and an input text message into output data in the form of a percentage of ethnic hatred in a test message of social Internet networks.