An intelligent chatbot for evaluating the emotional colouring of a message and responding accordingly

V. R. Kobchenko, V.M. Shymkovysh, P.I. Kravets, A.O. Novatskyi, L.L. Shymkovysh, А.Yu. Doroshenko


A recurrent neural network model, a database designed for neural network training, and a software tool for interacting with a bot have all been created. A large dataset (50 thousand comments) containing different reviews and sentiments was collected and annotated to successfully train and validate the model. It was also translated into Ukrainian language with the help of an automatic translator. The architecture of the neural network model underwent optimization to enhance classification outcomes. Furthermore, work was conducted on enhancing the user interface. The developed application was tested, and the results were demonstrated. The resulting model demonstrated accuracy 85% in determining sentiments. The implemented application has got basic design (which can be customized) and some settings for chatbot. Further improvement of the model’s classification quality can be achieved by collecting a larger and better organised dataset or by researching other RNN architectures.

Problems in programming 2024; 1: 23-29


recurrent neural networks; sentiment analysis; Python; tensorflow; keras; chatbot

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