Труды КНЦ. Технические науки. – 2026. - № 1

Труды Кольского научного центра РАН. Серия: Технические науки. 2026. Т. 17, № 1. С. 105-114. Transactions of the Kola Science Centre of r A s . Series: Engineering Sciences. 2026. Vol. 17, No. 1. P. 105-114. Bhamare B. R., Prabhu J. A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas // PeerJ Computer Science. 2021. Vol. 7. Art. e347. Bouazizi M., Ohtsuki T. Opinion Mining in Twitter How to Make Use of Sarcasm to Enhance Sentiment Analysis // Proceedings o f the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. Paris: ACM, 2015. P. 1594-1597. Dulhare U., A r ifF. A Machine Learning Based Approach for Opinion Mining on Social Network Data // Computer Communication, Networking and Internet Security. Springer Singapore. 2017. P. 135-148. Hybrid Machine Learning Techniques for Comparative Opinion Mining / B. O. Ondara [et al.] // Indonesian Journal of Artificial Intelligence and Data Mining. 2023. Vol. 6, № 2. P. 131-143. Joke N. Intent Analysis On Reviews: Unveiling Consumer Sentiment, Behavior Patterns, and Market Insights // SSRN Electronic Journal. 2025. DOI: 10.2139/ssrn.5338834. Najar D., Mesfar S. Opinion mining and sentiment analysis for Arabic on-line texts: application on the political domain // International Journal of Speech Technology. 2017. Vol. 20, № 3. P. 575-585. Oksyuta O. V., Yudin A. V. Using machine learning to analyze the emotional tone o f Russian-language product reviews // Materials o f the All-Russian Scientific and Practical Conference “Challenging Issues in Systems Modeling and Processes”. FSBE Institution o f Higher Education Voronezh State University of Forestry and Technologies named after G. F. Morozov, 2025. P. 385-391. Opinion mining of student regarding educational system using online platform / M. Irfan [et al.] // Conclusions in Medicine. 2025. Vol. 3, iss. 2. P. 112-125. Perikos I., Hatzilygeroudis I. Aspect based sentiment analysis in social media with classifier ensembles // 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS). 2017. P. 273-278. Pimeshkov V., NikonorovaM., ShishaevM. Technology for Forming a Multilayer Knowledge Graph to Determine the Relevance of Documents // Digital and Information Technologies in Economics and Management / Gibadullin A. Cham: Springer Nature Switzerland, 2025. Vol. 1422. P. 134-152. Public opinion mining in social media about Ethiopian broadcasts using deep learning / M. Yibeyin [et al.] // Scientific Reports. 2024. Vol. 14. Art. 27676. Sarkar S. A Comparative Study o f Different Natural Language Processing Techniques for Sentiment Analysis and Opinion Mining // Iconic Research and Engineering Journals. 2025. Vol. 8, iss. 12. P. 1695-1708. Taxonomy of OpinionMining, Approaches andDomain Applications: Future ResearchDirection / B. C. Ujah-Ogbuagu [et al.] // SN Comput. Sci. 2025. Vol. 6, № 6. P. 653. The OL-DA WE model: tweet polarity sentiment analysis with data augmentation / W. Wang [et al.] // Proceedings of the 2020 International Conference on Artificial Intelligence and Computer Engineering. 2020. Vol. 8. P. 40118-40128. Tian L., Lai C., Moore J. D. Polarity and Intensity: The Two Aspects of Sentiment Analysis: arXiv:1807.01466.arXiv, 2018. Varathan K. D., Giachanou A., Crestani F. Comparative opinion mining: A review // Journal of the Association for Information Science and Technology. 2017. Vol. 68, № 4. P. 811-829. R eferences Ahmed S., Danti A. Effective Sentimental Analysis and Opinion Mining of Web Reviews Using Rule Based Classifiers. Computational Intelligence in Data Mining— Volume 1. H. S. Behera, D. P. Mohapatra. New Delhi, Springer India, 2016, pp. 171-179. Amangeldi D., Usmanova A., Shamoi P. Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data. IEEE Access, 2024, Vol, 12, pp. 33504-33523. Basiron H., Kasmuri E. Subjectivity analysis in opinion mining—A systematic literature review. International Journal o fAdvances in Soft Computing and Its Applications, 2017, Vol. 9, No. 3, pp. 132-159. Behdenna S., Barigou F., Belalem G. Sentiment analysis at document level. International Conference on Smart Trendsfo r Information Technology and Computer Communications (SMARTCom 2016), 2016, pp. 682-689. Bhamare B. R., Prabhu J. A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas. PeerJ Computer Science, 2021, Vol. 7, Art. e347. Bouazizi M., Ohtsuki T. Opinion Mining in Twitter How to Make Use o f Sarcasm to Enhance Sentiment Analysis. Proceedings o f the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. Paris, ACM, 2015, pp. 1594-1597. Dulhare U., A r ifF. A Machine Learning Based Approach for Opinion Mining on Social Network Data. Computer Communication, Networking and Internet Security. Springer Singapore, 2017, pp. 135-148. © Никонорова М. Л., Шишаев М. Г., Штангель В. А., 2026 1 1 3