kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-1-52-67kaz29-2499Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEГИБРИДНЫЙ ПОДХОД К АНАЛИЗУ ТОНАЛЬНОСТИ ЦИТИРОВАНИЯ НА ОСНОВЕ ЛИНГВИСТИЧЕСКИХ ПРИЗНАКОВ И МАШИННОГО ОБУЧЕНИЯA HYBRID APPROACH TO THE ANALYSIS OF CITATION TONALITY BASED ON LINGUISTIC FEATURES AND MACHINE LEARNINGhttps://orcid.org/0000-0003-4439-7313АхмедияроваА. Т.AkhmediyarovaA. T.

PhD, профессор

г. Алматы

PhD, Professor

Almaty

a.akhmediyarova@satbayev.universityhttps://orcid.org/0000-0001-9565-5621АлибиеваЖ. М.AlibiyevaZh. M.

PhD, ассоциированный профессор

г. Алматы

PhD, Associate Professor

Almaty

zh.alibiyeva@satbayev.universityhttps://orcid.org/0000-0003-4975-6493ОралбековаД. О.OralbekovaD. O.

PhD, м.н.с.

г. Алматы

PhD, Junior Researcher

Almaty

dinaoral@mail.ruhttps://orcid.org/0000-0001-6004-103XНаурызбаеваА. И.NauryzbayevaA. I.

ст. преподаватель

г. Алматы

Senior Lecturer

Almaty

a.nauryzbaeva@satbayev.universityhttps://orcid.org/0000-0001-6004-103XКасымоваД. Т.KassymovaD. T.

PhD, ассистент-профессор

г. Алматы

PhD, Head of the Department, Assistant Professor

Almaty

d.kassymova@alt.edu.kzКазахский национальный исследовательский технический университет им. К.И. СатпаеваКазахстанSatbayev University, Kazakh National Research Technical University named after K.I. SatpayevKazakhstanКазахский национальный исследовательский технический университет им. К.И. СатпаеваКазахстан1Satbayev University, Kazakh National Research Technical University named after K.I. SatpayevKazakhstanИнститут информационных и вычислительных технологийКазахстанInstitute of Information and Computational TechnologiesKazakhstanALT Университет им. М. ТынышпаеваКазахстанALT University named after M. TynyshpayevKazakhstan2026290320262315267Copyright © Ахмедиярова А.Т., Алибиева Ж.М., Оралбекова Д.О., Наурызбаева А.И., Касымова Д.Т., 20262026Ахмедиярова А.Т., Алибиева Ж.М., Оралбекова Д.О., Наурызбаева А.И., Касымова Д.Т.Akhmediyarova A.T., Alibiyeva Z.M., Oralbekova D.O., Nauryzbayeva A.I., Kassymova D.T.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2499

Анализ тональности научных текстов, включая цитирования, активно развивается, позволяя выявлять эмоциональную окраску ссылок и их влияние на научный дискурс. Настоящее исследование направлено на разработку и оценку гибридного подхода, интегрирующего лингвистические правила (анализ частей речи, синтаксических зависимостей и отрицаний) с алгоритмами машинного обучения (SVM, RF, NB, J48) для классификации тональности цитат. Эксперименты проведены на корпусах ACL Anthology (8700 предложений) и Clinical Trials (дополнительно 6500 предложений) с использованием стратифицированного разбиения (70/15/15 для train/val/test) и 5-кратной кросс-валидации. Разработанный метод достиг 90% macro-F1 и 95% F1-меры на датасете Athar, а на Clinical Trials – 85% macro-F1, демонстрируя улучшение на 10–15% по сравнению с базовыми моделями (BERT, LSTM). Абляционные исследования подтвердили вклад лингвистиче- ских правил (рост F1 на 5–7% при их исключении). Тесты статистической значимости (McNemar, p<0.05) подтвердили устойчивость результатов. Предложенный подход эффективен для автоматического анализа цитирований и оценки научного влияния.

The analysis of tonality in scientific texts, including citations, is actively advancing, enabling the identification of emotional coloring in references and their impact on scientific discourse. This study focuses on developing and evaluating a hybrid approach that integrates linguistic rules (analysis of parts of speech, syntactic dependencies, and negations) with machine learning algorithms (SVM, RF, NB, J48) to classify citation tonality. Experiments were conducted on the ACL Anthology (8700 sentences) and Clinical Trials (6500 additional sentences) corpora using stratified splitting (70/15/15 for train/val/test) and 5-fold cross-validation. The proposed method achieved 90% macro-F1 and 95% F1-score on the Athar dataset, and 85% macro-F1 on Clinical Trials, showing a 10–15% improvement over baseline models (BERT, LSTM). Ablation studies confirmed the contribution of linguistic rules (F1 increase of 5–7% when excluded). Statistical significance tests (McNemar, p<0.05) validated the robustness of the results. The approach proves effective for automated citation analysis and scientific impact assessment.

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The authors declare that there are no conflicts of interest present.