TY - GEN
T1 - Sentiment in academic texts
AU - Solovyev, Valery
AU - Solnyshkina, Marina
AU - Gafiyatova, Elzara
AU - McNamara, Danielle
AU - Ivanov, Vladimir
N1 - Funding Information:
This research was financially supported by the Russian Science Foundation, grant #18-18-00436, the Russian Government Program of Competitive Growth of Kazan Federal University, and the subsidy for the state assignment in the sphere of scientific activity, grant agreement #34.5517.2017/6.7. The Russian Academic Corpus (Section II.A) was created without the support of the Russian Science Foundation.
Publisher Copyright:
© 2019 FRUCT.
PY - 2019/5/9
Y1 - 2019/5/9
N2 - 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.
AB - 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.
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U2 - 10.23919/FRUCT.2019.8711900
DO - 10.23919/FRUCT.2019.8711900
M3 - Conference contribution
AN - SCOPUS:85066427524
T3 - Conference of Open Innovation Association, FRUCT
SP - 408
EP - 414
BT - Proceedings of the 24th Conference of Open Innovations Association FRUCT, FRUCT 2019
PB - IEEE Computer Society
T2 - 24th Conference of Open Innovations Association FRUCT, FRUCT 2019
Y2 - 8 April 2019 through 12 April 2019
ER -