International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 1, 8 Feb. 2025
Cover page and Table of Contents: PDF (size: 4039KB)
Intelligent System for Recognizing Tone and Categorizing Text in Media News at an Electronic Business Based on Sentiment and Sarcasm Analysis
PDF (4039KB), PP.90-139
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Author(s)
Danylo Holubinka
1
Victoria Vysotska
1
Serhii Vladov
2
Yuriy Ushenko
3,*
Mariia Talakh
4
Yurii Tomka
3
Index Terms
Large Language Models, Recognizing Tone, Categorizing Text, NLP, Media News, Intelligent System, Information Engineering, Natural Language Processing, Electronic Business, Sentiment Analysis, LLM, RNN, Sarcasm Analysis, Recurrent Neural Networks
Abstract
During the implementation of the work on the creation of the system of tonality recognition and text categorization in the news, a study of the subject area was conducted, which allowed the understanding of the processes of text analysis in the mass media to be enriched. The necessary data for further processing was found. The work resulted from a program that consists of an information parser, a data analyser and cleaner, a Large Language Models model, a neural network, and a database with vectorized data. These components were integrated into the user interface and implemented as a program window. The program can analyse news texts, determining their tone and categories. At the same time, it provides the user with a convenient interface for entering text and receiving analysis results. Therefore, the created system is a powerful tool for automated analysis of textual data in mass media, which can be used for various purposes, including monitoring the news space, analysis of public opinion, and others. Also, the developed information technology successfully meets the set tasks aimed at tonality analysis and categorization of news. It effectively solves the task of collecting, analysing and classifying news materials, which allows users to receive operational and objective information. Its architecture and functionality allow for easy changes and additions in the
future, making it a flexible and adaptable tool for news analytics and decision-making in various business sectors.
Cite This Paper
Danylo Holubinka, Victoria Vysotska, Serhii Vladov, Yuriy Ushenko, Mariia Talakh, Yurii Tomka, "Intelligent System for Recognizing Tone and Categorizing Text in Media News at an Electronic Business Based on Sentiment and Sarcasm Analysis", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.1, pp. 90-139, 2025. DOI:10.5815/ijieeb.2025.01.06
Reference
[1]T. Kanan, et al. "Business intelligence using deep learning techniques for social media contents." Cluster Computing 26.2 (2023): 1285-1296.
[2]M. R. Alam, et al. "Social media content categorization using supervised based machine learning methods and natural language processing in bangla language." 11th International Conference on Electrical and Computer Engineering (ICECE). IEEE, 2020.
[3]V. Dogra, et al. "A Complete Process of Text Classification System Using State‐of‐the‐Art NLP Models." Computational Intelligence and Neuroscience 2022.1 (2022): 1883698.
[4]B. Bengfort, R. Bilbro, and Tony Ojeda. Applied text analysis with Python: Enabling language-aware data products with machine learning. " O'Reilly Media, Inc.", 2018.
[5]V. Vysotska, et al. "NLP tool for extracting relevant information from criminal reports or fakes/propaganda content." 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2022.
[6]W. H. Bangyal, et al. "Detection of Fake News Text Classification on COVID‐19 Using Deep Learning Approaches." Computational and mathematical methods in medicine 2021.1 (2021): 5514220.
[7]M. Haider, and A. Gandomi. "When big data made the headlines: Mining the text of big data coverage in the news media." International Journal of Services Technology and Management 27.1-2 (2021): 23-50.
[8]B. Kratzwald, et al. "Deep learning for affective computing: Text-based emotion recognition in decision support." Decision support systems 115 (2018): 24-35.
[9]V. Vysotska, et al. "Tone Analysis of Regional Articles in English-Language Newspapers Based on Recurrent Neural Network Bi-LSTM." 2023 IEEE 5th International Conference on Advanced Information and Communication Technologies (AICT). IEEE, 2023, pp. 158-163.
[10]S. Voloshyn, et al. "Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool." Proceedings of the IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 2022, pp. 83-88, doi: 10.1109/CSIT56902.2022.10000627.
[11]M. Hasan, et al. "Leveraging textual information for social media news categorization and sentiment analysis." Plos one 19.7 (2024): e0307027.
[12]V. Vysotska, et al. "Sentiment Analysis of Information Space as Feedback of Target Audience for Regional E-Business Support in Ukraine." CEUR Workshop Proceedings, Vol-3426, 2023, 488-513.
[13]N. Khairova, et al. "Models for effective categorization and classification of texts into specific thematic groups (using gender and criminal themes as examples)." CEUR Workshop Proceedings 3722 (2024) 37-49.
[14]L. Chyrun, et al. "The Electronic Digests Formation and Categorization for Textual Commercial Content." CEUR Workshop Proceedings, Vol-2870, 2021, pp. 1816-1831.
[15]V. Starko, Semantic Annotation for Ukrainian: Categorization Scheme, Principles, and Tools." CEUR workshop proceedings, Vol-2604, 239-248. (2020).
[16]A. Berko, et al. "The text classification based on Big Data analysis for keyword definition using stemming." Proceedings of IEEE 16th International conference on computer science and information technologies, Lviv, 22–25 September 2021, pp. 184–188.
[17]O. Barkovska, et al. "A Conceptual Text Classification Model Based on Two-Factor Selection of Significant Words." CEUR Workshop Proceedings, Vol-3396, 2023, 244-255.
[18]A. Sachenko, et al. "Method of Determining the Text Sentiment by Thematic Rubrics." CEUR Workshop Proceedings 3688 (2024) 404-414.
[19]R. Nazarchuk, and S. Albota, Tweets about Ukraine during the russian-Ukrainian War: Quantitative Characteristics and Sentiment Analysis." CEUR Workshop Proceedings, Vol-3426, 2023, 551-560.
[20]O. Zalutska, et al. "Method for Sentiment Analysis of Ukrainian-Language Reviews in E-Commerce Using RoBERTa Neural Network." CEUR Workshop Proceedings, Vol-3387, 2023, pp. 344-356.
[21]T. Olaleye, et al. "Evaluation of Vader and MultiLingual sentiment analyzers for opinion analytics using graphical illustrations." CEUR Workshop Proceedings, Vol-3171, 2022, pp. 150-160
[22]S. Kubinska, et al. "Ukrainian Language Chatbot for Sentiment Analysis and User Interests Recognition based on Data Mining." CEUR Workshop Proceedings, Vol-3171, 2022, pp. 315-327
[23]Pavlo Radiuk, et al. "An Ensemble Machine Learning Approach for Twitter Sentiment Analysis." CEUR Workshop Proceedings, Vol-3171, 2022, pp. 387-397
[24]Z. Kochuieva, et al. "Usage of Sentiment Analysis to Tracking Public Opinion." CEUR Workshop Proceedings, Vol-2870, 2021, pp. 272-285.
[25]O. Artemenko, et al. "Using sentiment text analysis of user reviews in social media for e-tourism mobile recommender systems." CEUR workshop proceedings, Vol-2604, 259-271. (2020)
[26]V. Bobicev, et al. "Sentiment Analysis in the Ukrainian and Russian News." First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 1050-1055. (2017)
[27]K. Shakhovska, et al. "The sentiment analysis model of services providers’ feedback." Electronics (Switzerland), 2020, 9(11), pp. 1–15, 1922.
[28]Neha Singh, Umesh Chandra Jaiswal, Ritu Singh, "Detecting Sarcasm Text in Sentiment Analysis Using Hybrid Machine Learning Approach", International Journal of Intelligent Systems and Applications, Vol.16, No.4, pp.72-85, 2024.
[29]Neny Sulistianingsih, I Nyoman Switrayana, "Enhancing Sentiment Analysis for the 2024 Indonesia Election Using SMOTE-Tomek Links and Binary Logistic Regression", International Journal of Education and Management Engineering, Vol.14, No.3, pp. 22-32, 2024.
[30]Zhengbing Hu, Ivan Dychka, Kateryna Potapova, Vasyl Meliukh, "Augmenting Sentiment Analysis Prediction in Binary Text Classification through Advanced Natural Language Processing Models and Classifiers", International Journal of Information Technology and Computer Science, Vol.16, No.2, pp.16-31, 2024.
[31]Shashank Mishra, Mukul Aggarwal, Shivam Yadav, Yashika Sharma, "An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model", International Journal of Engineering and Manufacturing, Vol.13, No.5, pp. 11-20, 2023.