Анализ социальных сетей и статистическая обработка твитов о COVID-19

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Анализируются сообщения в «Твиттере» для выделения наиболее актуальных тем, связанных с распространением коронавируса в мире на примере русскоязычного сегмента «Твиттера». Для анализа близости слов в тексте и формирования терминологических цепочек слов использовался метод машинного обучения word2vec. Были выделены наиболее распространенные n-граммы, связанные с COVID-19. Авторы предлагают оригинальную методику выявления тематических групп твитов и оценки их значимости, которую можно использовать не только для коротких сообщений в «Твиттере», но и для анализа текстовых документов. В результате исследования выделяются n-граммы различной длины, проводится их статистический анализ, формируются тематические группы, включающие наиболее релевантные n-граммы, и определяются их веса. Для оценки популярности тем предлагается численный показатель. Учитывается актуальная популярность тем и их динамика. Отмечается, что происходит эволюция тем обсуждений и образование новых терминов, связанных с пандемией.

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Анализ социальных сетей, covid-19, n-грамм, твиты, тематическая группа

Короткий адрес: https://sciup.org/148324976

IDR: 148324976   |   DOI: 10.18137/RNU.V9187.22.02.P.084

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