Identification and Classification of Toxic Statements by Machine Learning Methods | Zendy

E. N. Platonov | Zendy; V.Y. Rudenko | Zendy

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Identification and Classification of Toxic Statements by Machine Learning Methods

Author(s) -

E. N. Platonov,

V.Y. Rudenko

Publication year - 2022

Publication title -

modelling and data analysis

Language(s) - English

Resource type - Journals

eISSN - 2311-9454

pISSN - 2219-3758

DOI - 10.17759/mda.2022120103

Subject(s) - word2vec , computer science , identification (biology) , task (project management) , offensive , artificial intelligence , machine learning , support vector machine , convolutional neural network , social media , artificial neural network , natural language processing , information retrieval , world wide web , operations research , engineering , botany , systems engineering , embedding , biology

The number of comments left on social media platforms can reach several million per day, so their owners are interested in automatic content filtering. In this paper, the task of identifying offensive statements in texts is considered. When solving the problem, various methods of vector text conversion were considered: TF-IDF, Word2Vec, Glove, etc. The results of the application of classical text classification methods and neural network methods (LSTM, CNN) were also considered and presented.

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