Methods of Machine Learning and Design of a System for Determining the Emotional Coloring of Ukrainian-language Content

2024;
: pp. 74 - 86
1
Lviv Polytechnic National University, Department of Information Systems and Networks
2
Lviv Polytechnic National University

In the article, the authors analyze the current state of research in the field of emotional analysis of Ukrainian-language content for data mining systems. The main methods and approaches to solving the problem are analyzed. The main machine learning algorithms for analyzing textual content are also considered. As a result of the analysis, the main methods and approaches that can be used to analyze the Ukrainian language were identified and classified. The next step was to design the system's functionality using a structural approach. The authors of the article have designed an information system using a structural approach. A contextual diagram of the information system was developed and its main process was decomposed in order to show in more detail the process of preparing and analyzing information in the process of determining the emotional coloring.

  1. Otamendi, J., F., & Martín, D. L. S. (2020, September 4). The Emotional Effectiveness of Advertisement.   Frontiers.                      Retrieved         February                      28,                              2024,                         from https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.02088/full
  2. Basyuk, T., & Vasyliuk, A. (2023). Peculiarities of an Information System Development for Studying Ukrainian Language and Carrying out an Emotional and Content Analysis. CEUR Workshop Proceedings, 3396, 279–294.
  3. Ou, L. C., Luo, M. R., Woodcock, A., & Wright, A. B. (2004). A study of colour emotion and colour preference. Part I: Colour emotions for single colours. Color Research & Application, 29(3), 232–240. https://doi.org/10.1002/col.20010
  4. Bessa, A. (2023, December 11). Lexicon-based sentiment analysis: What it is & how to conduct one. Knime. Retrieved February 28, 2024, from https://www.knime.com/blog/lexicon-based-sentiment-analysis
  5. Fagbola, T. M., & Surendra Colin, T. (2019). Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter. International Journal of Advanced Computer Science and Applications, 10(10), 329–336. https://doi.org/10.14569/ijacsa.2019.0101047
  6. Guzsvinecz, T., & Szűcs, J. (2023). Length and sentiment analysis of reviews about top-level video game genres on the steam platform. Computers in Human Behavior, 149, A107955. https://doi.org/10.1016/j.chb.2023.107955
  7. Kirti, A. (2023, April 17). Rule Based Approach in NLP. Geeksforgeeks. Retrieved February 28, 2024, from     https://www.geeksforgeeks.org/rule-based-approach-in-nlp/
  8. Koukaras, P., Rousidis, D., & Tjortjis, C. (2023). Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths. Informatics, 10(4), A88. https://doi.org/10.3390/informatics10040088
  9. Pragnya, S. S. (2022, January 16). VADER (Valence Aware Dictionary and sentiment Reasoner) Sentiment Analysis. Medium. Retrieved  February 28, 2024, from https://swayanshu.medium.com/vader-valence- aware-dictionary-and-sentiment-reasoner-sentiment-analysis-28251536698
  10. Barik, K., & Misra, S. (2024). Analysis of customer reviews with an improved VADER  lexicon classifier. Journal of Big Data, 11(1), A10. https://doi.org/10.1186/s40537-023-00861-x
  11. (n.d.). A Guide on Word  Embeddings in NLP. Turing. Retrieved  February 28, 2024, from https://www.turing.com/kb/guide-on-word-embeddings-in-nlp
  12. Moudhich, I., & Fennan, A. (2024). Graph embedding approach to analyze sentiments on cryptocurrency. International Journal of Electrical and Computer Engineering, 14(1), 690–697. https://doi.org/10.11591/ijece.v14i1.pp690- 697
  13. Hicham, N., Nassera, H., & Karim, S. (2024). Enhancing Arabic  E-Commerce Review Sentiment Analysis Using a hybrid Deep Learning Model and FastText word embedding. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/10.4108/eetiot.4601
  14. Su, Y., & Kabala, Z. J. (2023). Public Perception of ChatGPT and Transfer  Learning for  Tweets Sentiment Analysis Using Wolfram Mathematica. Data, 8(12), A180. https://doi.org/10.3390/data8120180
  15. (n.d.). What is Supervised Learning? Google Cloud. Retrieved February 28, 2024, from https://cloud.google.com/discover/what-is-supervised-learning
  16. Aysan, A. F., Caporin, M., & Cepni, O. (2024). Not all words are equal: Sentiment and jumps in the cryptocurrency market. Journal of International Financial Markets, Institutions and Money, 91, A101920. https://doi.org/10.1016/j.intfin.2023.101920
  17. Labd, Z., Bahassine, S., & Housni, K. (2024). Ext classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: A comparative study. International Journal of Electrical and Computer Engineering, 14(1), 589–599. https://doi.org/10.11591/ijece.v14i1.pp589-599
  18. Tabany, M., & Gueffal, M. (2024). Sentiment Analysis and Fake Amazon Reviews Classification Using SVM Supervised Machine Learning Model. Journal of Advances in Information Technology, 15(1), 49–58. https://doi.org/10.12720/jait.15.1.49-58
  19. Liu, J., & Si, J. (2024). Digitization of Civics in College Physical Education Courses Based on the Correlation Matrix. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.01576