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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">dgisocio</journal-id><journal-title-group><journal-title xml:lang="ru">Цифровая социология/Digital Sociology</journal-title><trans-title-group xml:lang="en"><trans-title>Digital Sociology</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2658-347X</issn><issn pub-type="epub">2713-1653</issn><publisher><publisher-name>Государственный университет управления</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26425/2658-347X-2023-6-1-48-58</article-id><article-id custom-type="elpub" pub-id-type="custom">dgisocio-235</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЦИФРОВАЯ СРЕДА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>DIGITAL ENVIRONMENT</subject></subj-group></article-categories><title-group><article-title>Социология ценностей: опыт построения таксономии путем использования технологии анализа естественного языка</article-title><trans-title-group xml:lang="en"><trans-title>Sociology of values: experience of building a taxonomy by using natural language analysis technology</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4509-8657</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Кашина</surname><given-names>М. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Kashina</surname><given-names>M. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кашина Марина Александровна, д-р полит. наук, проф. каф. социальных технологий</p><p>г. Санкт-Петербург</p></bio><bio xml:lang="en"><p>Marina A. Kashina, Dr. Sci. (Polit.), Prof. at the Social Technologies Department</p><p>St. Petersburg</p></bio><email xlink:type="simple">kashina-ma@ranepa.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5016-9187</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ткач</surname><given-names>С.</given-names></name><name name-style="western" xml:lang="en"><surname>Tkach</surname><given-names>S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ткач Сергей, программист</p><p>г. Санкт-Петербург</p></bio><bio xml:lang="en"><p>Sergey Tkach, Programmer</p><p>St. Petersburg</p></bio><email xlink:type="simple">sergey@ngostellit.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Северо-Западный институт управления – филиал Российской академии народного хозяйства и государственной службы при Президенте Российской Федерации</institution><country>Россия</country></aff><aff xml:lang="en"><institution>North-West Institute of Management – Branch of the Russian Presidential Academy of National Economy and Public Administration</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Научный парк Санкт-Петербургского государственного университета</institution><country>Россия</country></aff><aff xml:lang="en"><institution>St. Petersburg University Research Park</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>27</day><month>04</month><year>2023</year></pub-date><volume>6</volume><issue>1</issue><fpage>48</fpage><lpage>58</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Кашина М.А., Ткач С., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Кашина М.А., Ткач С.</copyright-holder><copyright-holder xml:lang="en">Kashina M.A., Tkach S.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://digitalsociology.guu.ru/jour/article/view/235">https://digitalsociology.guu.ru/jour/article/view/235</self-uri><abstract><p>Современные исследования в области социологии науки становятся все более сложными из-за постоянно растущей публикационной активности авторов. Для отслеживания трендов в отраслевых социологиях ученые обращаются к методам наукометрии, однако их оказывается недостаточно. Предметом исследования выступила тенденции развития социологии ценностей как отрасли социологии. Цель статьи – оценка возможностей использования методов анализа естественного языка NLP/NLA (англ. natural language processing – обработка текста на естественном языке, natural language assessment – оценка естественного языка) для тематической и теоретической кластеризации исследований в социологии ценностей. Дизайн исследования количественно-качественный, работа проводилась в два этапа. На первом этапе с помощью интеллектуального анализа текста проанализирована 121 аннотация научной статьи, после чего их общий массив был разбит на кластеры. На втором этапе результаты машинной кластеризации проверялись методом качественного анализа текста, на основе чего выделялись ограничения и возможности метода NLP/NLA для решения задачи кластеризации научных текстов. В ходе анализа обнаружено, что статьи с более консервативным ядром теоретических категорий (гендерные исследования, миграционные исследования, теория глобализма) лучше поддаются кластеризации, а теории со слабо структурированным теоретическим ядром (теории с использованием экологической терминологии, теории неравенства) поддаются кластеризации значительно хуже. Полученные результаты позволяют сформировать новое направление работы с большими массивами научных текстов, связанное с их кластеризацией с помощью NLP/NLA. Построение кластеров дает возможность исследователям изучать все тексты данной тематической области, а не только самые цитируемые. Это, в свою очередь, обеспечивает видимость всех научных идей, в том числе не получивших популярности/известности.</p></abstract><trans-abstract xml:lang="en"><p>Modern research in the field of sociology of science is becoming more complicated due to the constantly growing publication activity of authors. To track trends in sectoral sociology, scientists turn to scientometric methods, but they are not enough. Trends in the development of the sociology of values as a branch of sociology are the subject of the study. The purpose of the work is an assessment of the possibilities of using natural language analysis methods (NLP/NLA) for thematic and theoretical clustering of research in the sociology of values. The design of the study was quantitative and qualitative, it was carried out in two stages. At the first stage, 121 abstracts of a scientific articles were analyzed using text mining, after which their total array was divided into clusters. At the second stage, the results of machine clustering were examined by the method of qualitative text analysis, on the basis of which the limitations and capabilities of the NLP/NLA method were identified for solving the problem of clustering scientific texts. It was found that articles with a more conservative core of theoretical categories (gender studies, migration studies, the theory of globalism) are more amenable to clustering, while theories with a loosely structured and fluid theoretical core (theories using environmental terminology, theories of inequality) are much less amenable to explicit clustering. The results obtained allow us to form a new direction of work with large arrays of scientific texts, associated with their clustering using NLP/NLA. Building clusters enables researchers to work with all texts in a given subject area, and not just with the most cited ones. This, in turn, provides the visibility of all scientific ideas, including those that have not gained popularity/notability.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>Смешанные методы</kwd><kwd>научный текст</kwd><kwd>кластеризация</kwd><kwd>социогуманитарное знание</kwd><kwd>обработка естественного языка</kwd><kwd>анализ естественного языка</kwd><kwd>NLP/NLA</kwd><kwd>метод Варда</kwd><kwd>искусственный интеллект</kwd><kwd>структурализм</kwd><kwd>метафора</kwd><kwd>акторная теория</kwd></kwd-group><kwd-group xml:lang="en"><kwd>Mixed methods</kwd><kwd>scientific text</kwd><kwd>clustering</kwd><kwd>socio-humanitarian knowledge</kwd><kwd>natural language processing</kwd><kwd>natural language assessment</kwd><kwd>Ward method</kwd><kwd>artificial intelligence</kwd><kwd>structuralism</kwd><kwd>metaphor</kwd><kwd>actor theory</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Александров Д.А. 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