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<front> <journal-meta>
<journal-id journal-id-type="publisher-id">Marketing and marketing research</journal-id>
<journal-title-group>
<journal-title xml:lang="en">Marketing and marketing research</journal-title>
<trans-title-group xml:lang="ru">
<trans-title>Маркетинг и маркетинговые исследования</trans-title>
</trans-title-group>
</journal-title-group>
<issn publication-format="print">2074-5095</issn>
<issn publication-format="electronic">2618-8872</issn>
<publisher>
<publisher-name xml:lang="en">BIBLIO-GLOBUS Publishing House</publisher-name>
</publisher>
</journal-meta><article-meta>
<article-id pub-id-type="publisher-id">125998</article-id>
<article-id pub-id-type="doi">10.18334/marketing.31.3.125998</article-id>
<article-id custom-type="edn" pub-id-type="custom">LGUMDF</article-id>
<article-categories>
<subj-group subj-group-type="toc-heading" xml:lang="en">
<subject>Articles</subject>
</subj-group>
<subj-group subj-group-type="toc-heading" xml:lang="ru">
<subject>Статьи</subject>
</subj-group>
<subj-group subj-group-type="article-type">
<subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title xml:lang="en">Разработка маркетинговых стратегий для повышения привлечения и удержания китайских туристов: анализ реальных отзывов и предложения для российских туристических агентств и органов управления</article-title>
<trans-title-group xml:lang="ru">
<trans-title>Developing marketing strategies using LDA topic modeling based on Chinese tourists' reviews of Russian attractions</trans-title>
</trans-title-group>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0009-0002-5019-9022</contrib-id><contrib-id contrib-id-type="spin">7793-2456</contrib-id>
<name-alternatives>
<name xml:lang="en">
<surname>Li</surname>
<given-names>Tianyou </given-names>
</name>
<name xml:lang="ru">
<surname>Ли</surname>
<given-names>Тянью </given-names>
</name>
</name-alternatives>
<bio xml:lang="ru">
<p>инженер-исследователь Центра кроссмедийных технологий Уральского гуманитарного института, аспирант</p>
</bio>
<email>supertianyou@163.com</email>
<xref ref-type="aff" rid="aff1"/>
</contrib>

<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0009-0002-4452-0967</contrib-id><contrib-id contrib-id-type="spin">7436-5633</contrib-id>
<name-alternatives>
<name xml:lang="en">
<surname>Xu</surname>
<given-names>Weichen </given-names>
</name>
<name xml:lang="ru">
<surname>Сюй</surname>
<given-names>Вэйчэнь </given-names>
</name>
</name-alternatives>
<bio xml:lang="ru">
<p>инженер-исследователь Центра кроссмедийных технологий Уральского гуманитарного института, аспирант</p>
</bio>
<email>rngchen163@163.com</email>
<xref ref-type="aff" rid="aff1"/>
</contrib>

<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0009-0001-9652-6191</contrib-id>
<name-alternatives>
<name xml:lang="en">
<surname>Zhang</surname>
<given-names>Dehua </given-names>
</name>
<name xml:lang="ru">
<surname>Чжан</surname>
<given-names>Дэхуа </given-names>
</name>
</name-alternatives>
<bio xml:lang="ru">
<p>аспирант</p>
</bio>
<email>522013709@qq.com</email>
<xref ref-type="aff" rid="aff1"/>
</contrib>
</contrib-group><aff-alternatives id="aff1">
<aff>
<institution xml:lang="en">Ural Federal University named after the first President of Russia B.N.Yeltsin</institution>
</aff>
<aff>
<institution xml:lang="ru">Уральский федеральный университет им. первого Президента России Б.Н. Ельцина</institution>
</aff>
</aff-alternatives>        
        
<pub-date date-type="pub" iso-8601-date="2026-09-30" publication-format="print">
<day>30</day>
<month>09</month>
<year>2026</year>
</pub-date>
<volume>31</volume>
<issue>3</issue>
<issue-title xml:lang="en">VOL 31, NO3 (2026)</issue-title>
<issue-title xml:lang="ru">ТОМ 31, №3 (2026)</issue-title>
<fpage></fpage>
<lpage></lpage>
<history>
<date date-type="received" iso-8601-date="2026-03-24">
<day>24</day>
<month>03</month>
<year>2026</year>
</date>
<date date-type="accepted" iso-8601-date="2026-05-05">
<day>05</day>
<month>05</month>
<year>2026</year>
</date>
</history>

<permissions>
<copyright-statement xml:lang="en">Copyright ©; 2026, Li T., Syuy V., Chzhan D.</copyright-statement>
<copyright-statement xml:lang="ru">Copyright ©; 2026, Ли Т., Сюй В., Чжан Д.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder xml:lang="en">Li T., Syuy V., Chzhan D.</copyright-holder>
<copyright-holder xml:lang="ru">Ли Т., Сюй В., Чжан Д.</copyright-holder>
<ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2026-09-30"/>
</permissions>



<self-uri xlink:href="https://1economic.ru/lib/125998">https://1economic.ru/lib/125998</self-uri>
<abstract xml:lang="en"><p>The rapid return of tourists from China to Russia has brought a key challenge: how to solve the structural deficiency of digital marketing and service infrastructure while the number of tourists has soared. Therefore, it is very important to solve the complex cognitive model and experience dimension of China tourists in order to transform high traffic into sustainable economic value. This paper proposes a strategic marketing framework based on big data text mining to deal with these structural bottlenecks. This paper uses Python to capture massive comments from Ctrip, and uses LDA theme modeling and emotional analysis to deconstruct tourists' experiences in Moscow, St. Petersburg and Vladivostok. The results reveal the dual psychological structure of Red Nostalgia and Aesthetic Worship, which shows a significant gap between high aesthetic satisfaction and low functional service experience. Therefore, it is suggested to adopt strategies such as digital ecosystem integration, intergenerational segmentation and service optimization to enhance the competitiveness of the industry</p>
</abstract>
<trans-abstract xml:lang="ru"><p>Быстрое возвращение туристов из Китая в Россию поставило перед нами ключевую задачу: как решить структурные проблемы цифрового маркетинга и сервисной инфраструктуры в условиях резкого роста числа туристов. Поэтому крайне важно решить сложную когнитивную модель и изучить особенности впечатлений китайских туристов, чтобы преобразовать большой поток туристов в устойчивую экономическую ценность. В данной статье предлагается стратегическая маркетинговая модель, основанная на анализе больших данных текста, для решения этих структурных проблем. В работе используется Python для сбора большого количества комментариев с сайта Ctrip, а также применяется тематическое моделирование LDA и эмоциональный анализ для деконструкции впечатлений туристов в Москве, Санкт-Петербурге и Владивостоке. Результаты показывают двойственную психологическую структуру «красной ностальгии» и «эстетического поклонения», демонстрирующую значительный разрыв между высокой эстетической удовлетворенностью и низким функциональным уровнем сервиса. Поэтому предлагается использовать такие стратегии, как интеграция цифровой экосистемы, межпоколенческая сегментация и оптимизация услуг, для повышения конкурентоспособности отрасли</p>
</trans-abstract>
<kwd-group xml:lang="en">
<kwd>Chinese Tourists to Russia; Tourist Experience; Big Data Text Mining; LDA Topic Model; Marketing Strategy</kwd></kwd-group><kwd-group xml:lang="ru">
<kwd>китайские туристы в России; туристический опыт; анализ больших данных текста; тематическое моделирование LDA; маркетинговая стратегия</kwd></kwd-group>
</article-meta>
</front>
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