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

Abstract


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


Keywords


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

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References


Rodríguez-Ibánez, M., Casánez-Ventura, A., Castejón-Mateos, F., & Cuenca-Jiménez, P. M. (2023). A review on sentiment analysis from social media platforms. Expert Systems with Applications, 119862.

https://doi.org/10.1016/j.eswa.2023.119862

Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems. Vol. 89, pp. 14-46. https://doi.org/10.1016/j.knosys.2015.06.015

Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems. Vol. 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134

Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review. Vol. 53(6), pp. 4335-4385.

https://doi.org/10.1007/s10462-019-09794-5

Do, H. H., Prasad, P. W., Maag, A., & Alsadoon, A. (2019). Deep learning for aspect-based sentiment analysis: a comparative review. Expert systems with applications. Vol. 118, pp. 272-299.

https://doi.org/10.1016/j.eswa.2018.10.003

Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science. Vol. 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), pp. 436-444. https://doi.org/10.1038/nature14539

Shymkovych V., Telenyk S., Kravets P. (2021) Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA. Neural Computing and Applications. vol. 33, no.15, pp. 9467-9479. https://doi.org/10.1007/ s00521-021-05706-3

Harumy, T. F., Zarlis, M., Effendi, S., & Lidya, M. S. (2021, August). Prediction using a neural network algorithm approach (a review). 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), Pekan, Malaysia, IEEE, pp. 325-330. https://doi.org/10.1109/ICSECS52883.2021.00066

Shymkovych, Volodymyr, Anatoliy Doroshenko, Tural Mamedov, and Olena Yatsenko (2022) Automated Design of an Artificial Neuron for Field-Programmable Gate Arrays Based on an Algebra-Algorithmic Approach. International Scientific Technical Journal "Problems of Control and Informatics" vol. 67, no. 5, pp. 61-72.

https://doi.org/10.34229/2786-6505-2022-5-6

Perera, N. N., & Ganegoda, G. U. (2023). A Comprehensive Review on Speech Synthesis Using Neural-Network Based Approaches. 2023 3rd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, IEEE, pp. 214-219 https://doi.org/10.1109/ICARC57651.2023.10145741

Bezliudnyi Y., Shymkovysh V., Doroshenko A.( 2021) Convolutional neural network model and software for classification of typical pests. Prombles in programming. Vol.4, pp. 95-102. https://doi.org/10.15407/pp2021.04.095

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia tools and applications. Vol. 82(3), pp. 3713-3744. https://doi.org/10.1007/s11042-022-13428-4

Kravets P., Nevolko P., Shymkovych V., Shymkovych L. (2020) Synthesis of High-Speed Neuro-Fuzzy-Controllers Based on FPGA. 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT). pp. 291-295. https://doi.org/10.1109/ATIT50783.2020.9349299

Chai, J., Zeng, H., Li, A., & Ngai, E. W. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications. Vol. 6, 100134. https://doi.org/10.1016/j.mlwa.2021.100134

Kravets, P., Novatskyi, A., Shymkovych, V., Rudakova, A., Lebedenko, Y., Rudakova, H. Neural Network Model for Laboratory Stand Control System Controller with Parallel Mechanisms. Lecture Notes on Data Engineering and Communications Technologies. Springer, Cham. 2023. Vol 181. pp. 47-58 https://doi.org/10.1007/978-3-031-36118-0_5

Y.S. Hryhorenko, V.M. Shymkovysh, P.I. Kravets, A.O. Novatskyi, L.L. Shymkovysh, A.Yu. Doroshenko. A convolutional neural network model and software for the classification of the presence of a medical mask on the human face. Problems in programming. 2023. Vol. 2. pp. 59-66. https://doi.org/10.15407/pp2023.02.059

Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation. Vol. 31(7), pp. 1235-1270. https://doi.org/10.1162/neco_a_01199

Bezliudnyi Y., Shymkovych V., Kravets P., Novatsky A., Shymkovych L. Pro-russian propaganda recognition and analytics system based on text classification model and statistical data processing methods. Адаптивні системи автоматичного управління: міжвідомчий науково-технічний збірник. 2023. № 1 (42), c. 15-31. https://doi.org/10.20535/1560-8956.42.2023.278923

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation. Vol. 9(8), pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review. Vol. 53, pp. 5929-5955 https://doi.org/10.1007/s10462-020-09838-1

Kader, N.I.A., Yusof, U.K., Khalid, M.N.A., Husain, N.R.N. (2023). A Review of Long Short-Term Memory Approach for Time Series Analysis and Forecasting. Lecture Notes in Networks and Systems. Vol 573. Springer, Cham. pp. 12-21 https://doi.org/10.1007/978-3-031-20429-6_2

Long, F., Zhou, K., & Ou, W. (2019). Sentiment analysis of text based on bidirectional LSTM with multi-head attention. IEEE Access,7, pp. 141960-141969. https://doi.org/10.1109/ACCESS.2019.2942614

Olah, C. (2015). Understanding LSTM networks. URL: https://colah.github.io/posts/2015-08-Understanding-LSTMs

R. Yu, S. Liu, X. Wang (2024) Dataset Distillation: A Comprehensive Review. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 46, no. 1, pp. 150-170. https://doi.org/10.1109/TPAMI.2023.3323376

IMDB Dataset of 50K Movie Reviews URL: https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews

TensorFlow. (n.d.). TensorFlow: An end-to-end open source machine learning platform. Retrieved from https://www.tensorflow.org

Keras. (n.d.). Keras: The Python deep learning API. Retrieved from https://keras.io

Google Colab https://colab.research.google.com


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