Kazan (Volga region) Federal University, KFU
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Form of presentationArticles in international journals and collections
Year of publication2019
  • Gafiyatova Elzara Vasilovna, author
  • Solnyshkina Marina Ivanovna, author
  • Solovev Valeriy Dmitrievich, author
  • Bibliographic description in the original language Solovyev V, Solnyshkina M, Gafiyatova E, Sentiment in academic texts//Conference of Open Innovation Association, FRUCT. - 2019. - Vol.2019-April, Is.. - P.408-414.
    Annotation The problem of sentiment analysis has been widely studied in the past several decades. The research in the area has been predominantly based on data collated from online messages, microblogs, reviews, etc. Significantly fewer studies have been conducted based on academic discourse and especially school textbooks. However, sentiment analysis of academic texts can help answer pressing issues relating the ways in which different referents are presented in contemporary Russian school textbooks. In this paper, we analyze the distribution of sentiment words and phrases in a Corpus of Russian school textbooks on History (Grades 10–11) and Social Sciences (Grades 5 – 11). The results of the study demonstrate that the discourse within (1) History textbooks used in the 10th and 11th grades of Russian schools and (2) Social Studies textbooks written by Nikitin for Russian schools (Grades 5 – 11) contains predominantly negative sentiment: the writers select negatively valenced words and prefer presenting negative referents. By contrast, the discourse within the set of Social Studies textbooks written by Bogolubov revealed a predominantly positive bias. The authors discuss the implications of these trends in relation to the potential impact of the tone of educational discourse on learning.
    Keywords sentiment analysis, school textbooks, Corpus
    The name of the journal Conference of Open Innovation Association, FRUCT
    URL https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066427524&doi=10.23919%2fFRUCT.2019.8711900&partnerID=40&md5=8215b34101ca20ff613d3a5694afab82
    Please use this ID to quote from or refer to the card https://repository.kpfu.ru/eng/?p_id=207477&p_lang=2

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