КФУ. Карточка публикации. Sentiment in academic texts

КАЗАНСКИЙ
ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ

RUS

- EN
- 中文
- ES

SENTIMENT IN ACADEMIC TEXTS

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2019

Язык

английский

Авторы, сотрудники КФУ

Гафиятова Эльзара Василовна, автор

Солнышкина Марина Ивановна, автор

Соловьев Валерий Дмитриевич, автор

Библиографическое описание на языке оригинала

Solovyev V, Solnyshkina M, Gafiyatova E, Sentiment in academic texts//Conference of Open Innovation Association, FRUCT. - 2019. - Vol.2019-April, Is.. - P.408-414.

Аннотация

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.

Ключевые слова

sentiment analysis, school textbooks, Corpus

Название журнала

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

Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку

https://repository.kpfu.ru/?p_id=207477

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Гафиятова Эльзара Василовна	ru_RU
dc.contributor.author	Солнышкина Марина Ивановна	ru_RU
dc.contributor.author	Соловьев Валерий Дмитриевич	ru_RU
dc.date.accessioned	2019-01-01T00:00:00Z	ru_RU
dc.date.available	2019-01-01T00:00:00Z	ru_RU
dc.date.issued	2019	ru_RU
dc.identifier.citation	Solovyev V, Solnyshkina M, Gafiyatova E, Sentiment in academic texts//Conference of Open Innovation Association, FRUCT. - 2019. - Vol.2019-April, Is.. - P.408-414.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=207477	ru_RU
dc.description.abstract	Conference of Open Innovation Association, FRUCT	ru_RU
dc.description.abstract	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.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	sentiment analysis	ru_RU
dc.subject	school textbooks	ru_RU
dc.subject	Corpus	ru_RU
dc.title	Sentiment in academic texts	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU