Virtual Communication and Social Networks Virtual Communication and Social Networks Виртуальная коммуникация и социальные сети 2782-4799 2782-4802 114295 10.21603/vcsn-2026-5-2-123-137 GLWLIJ Лингводидактика в социальных сетях Linguodidactics in Social Networks Лингводидактика в социальных сетях Comparative Pedagogy of Sentiment Analysis in Digital Linguistics Сравнительная педагогика сентимент-анализа в цифровой лингвистике https://orcid.org/0000-0003-3632-793X Дмитриев Александр Владиславович Dmitriev Aleksandr Vladislavovich https://orcid.org/0009-0007-3127-2737 Крупнова Елена Сергеевна Krupnova Elena Sergeevna krupnalena@mail.ru https://orcid.org/0009-0002-9876-9477 Лаврентьева Екатерина Петровна Lavrent'eva Ekaterina Petrovna Санкт-Петербургский политехнический университет Петра Великого (Россия, Санкт-Петербург) Россия Peter the Great St. Petersburg Polytechnic University (Russia, St. Petersburg) Russian Federation Санкт-Петербургский политехнический университет Петра Великого (Россия, Санкт-Петербург) Россия Peter the Great St. Petersburg Polytechnic University (Russia, St. Petersburg) Russian Federation Санкт-Петербургский политехнический университет Петра Великого (Россия, Санкт-Петербург) Россия Peter the Great St. Petersburg Polytechnic University (Russia, St. Petersburg) Russian Federation 10 04 2026 10 04 2026 5 2 123 137 03 02 2026 30 03 2026 https://moloprom.kemsu.ru/en/nauka/article/114295/view

Цель работы – описать и методически обосновать опыт проведения практических занятий по анализу тональности текста, которые направлены на формирование у студентов компетенций в области применения словарных методов, готовых программных библиотек и нейросетевых языковых моделей. Исследование проводилось в рамках дисциплины «Компьютерная лингвистика» с 16 магистрантами первого курса направления «Интеллектуальные системы в гуманитарной среде» (специализация «Цифровая лингвистика») Санкт-Петербургского политехнического университета Петра Великого в 2024–2025 учебном году. Особое внимание уделено дидактическому дизайну заданий, позволяющему поэтапно усложнять задачи и сочетать освоение теоретических основ с развитием практических навыков. Первый модуль (первый этап) обучения был ориентирован на использование словарей тональности для английского и русского языков, что позволило студентам выявить фундаментальные ограничения лексикографического подхода: неполноту словарей, игнорирование контекста, трудности учета отрицаний, сарказма и культурной специфики. Второй модуль (второй этап) включал обучение модели DistilBERT и работу с различными датасетами, что дало возможность осмыслить роль данных в качестве предсказаний, а также ограничения современных нейросетевых архитектур при анализе сложных семантических явлений. Завершающим этапом стала групповая проектная защита, где студенты сравнивали три подхода – словарный, нейросетевой и библиотечный (VADER, TextBlob, Flair), – анализируя их преимущества, недостатки и применимость для практических задач. В результате установлено, что обучение способствовало развитию критического мышления и способности к аргументированному выбору инструментов в зависимости от специфики задачи и доступных ресурсов. Выявлено, что комплексное сочетание методов обеспечивает наиболее эффективную подготовку специалистов, поскольку позволяет осознать как сильные стороны, так и ограничения различных подходов. Результаты исследования подтверждают значимость интеграции анализа тональности в образовательные программы цифровой лингвистики как актуальной задачи современного высшего образования.

The article introduces an experiment conducted during classes on text tonality as part of a Master’s Degree course of Computational Linguistics. These classes develop competencies in vocabulary methods, ready-made software libraries, and neural network language models. The experiment involved 16 first-year Digital Linguistics students majoring in Smart Systems in the Humanities at St. Petersburg Polytechnic University in 2024–2025. The didactic design of assignments made it possible to gradually increase the complexity of tasks and combine the acquisition of theoretical knowledge with practical skill development. Module 1 focused on the use of English and Russian tonality dictionaries. The students learned to identify the fundamental limitations of the lexicographic approach, e.g., incomplete dictionaries, as well as difficulties associated with contextual meaning, negations, sarcasm, and cultural specificity. Module 2 involved training a DistilBERT model and working with various datasets. The students reflected on the role of data in prediction quality, as well as on the limitations of modern neural architectures in analyzing complex semantic phenomena. Module 3 involved a group project. The students compared lexicon-based, neural, and library-based (VADER, TextBlob, Flair) approaches for advantages, disadvantages, and applicability. The experiment fostered critical thinking and the ability to make informed decisions when electing tools for specific tasks and available resources. A comprehensive synergy of methods is efficient as it allows students to test both the strengths and the weaknesses of each approach. Integrating sentiment analysis into digital linguistics curricula is a key challenge for modern higher education.

 компьютерная лингвистика анализ тональности сентимент-анализ цифровые методы в  лингвистике большие языковые модели словари тональности методика преподавания computational linguistics opinion mining sentiment analysis digital methods in linguistics large language models sentiment lexicons teaching methodology

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