КФУ. Карточка публикации. Sentiment in Academic Texts

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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 Valery, Solnyshkina Marina, Gafiyatova Elzara, Batyrshin Ildar. Sentiment in Academic Texts//Proceedings of the 24th Conference of Open Innovations Association (FRUCT). - 2019. - Vol., Is.. - P.408-414.

Аннотация

The problem of sentiment analysis has been widely studied in the past decades. The research in the area was predominantly based on the data received from online messages, microblogs, reviews, etc. Significantly fewer studies were conducted based on academic discourse and especially school textbooks. However, sentiment analysis of academic texts can help answer pressing issues relating the ways different referents are presented in contemporary Russian school textbooks. In this paper we analyze distribution of sentiment words and phrases in the Corpus of Russian school textbooks on History (Grades 10–11) and Social Sciences (Grades 5 – 11). The results of the study convincingly demonstrate that the discourses of (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) bear predominantly negative sentiment: the writers select negatively valenced words and prefer presenting negative referents. The discourse of the set of Social Studies textbooks written by Bogolubov, judged by the choice of words and phrases, demonstrated a predominantly positive bias.

Ключевые слова

sentiment analysis, academic texts, distribution, positively, negatively colored words, grade levels

Название журнала

PROCEEDINGS OF THE 24TH CONFERENCE OF OPEN INNOVATIONS ASSOCIATION (FRUCT)

URL

https://fruct.org/publications/fruct24/files/Sol.pdf

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https://repository.kpfu.ru/?p_id=204621

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 Valery, Solnyshkina Marina, Gafiyatova Elzara, Batyrshin Ildar. Sentiment in Academic Texts//Proceedings of the 24th Conference of Open Innovations Association (FRUCT). - 2019. - Vol., Is.. - P.408-414.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=204621	ru_RU
dc.description.abstract	PROCEEDINGS OF THE 24TH CONFERENCE OF OPEN INNOVATIONS ASSOCIATION (FRUCT)	ru_RU
dc.description.abstract	The problem of sentiment analysis has been widely studied in the past decades. The research in the area was predominantly based on the data received from online messages, microblogs, reviews, etc. Significantly fewer studies were conducted based on academic discourse and especially school textbooks. However, sentiment analysis of academic texts can help answer pressing issues relating the ways different referents are presented in contemporary Russian school textbooks. In this paper we analyze distribution of sentiment words and phrases in the Corpus of Russian school textbooks on History (Grades 10–11) and Social Sciences (Grades 5 – 11). The results of the study convincingly demonstrate that the discourses of (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) bear predominantly negative sentiment: the writers select negatively valenced words and prefer presenting negative referents. The discourse of the set of Social Studies textbooks written by Bogolubov, judged by the choice of words and phrases, demonstrated a predominantly positive bias.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	sentiment analysis	ru_RU
dc.subject	academic texts	ru_RU
dc.subject	distribution	ru_RU
dc.subject	positively	ru_RU
dc.subject	negatively colored words	ru_RU
dc.subject	grade levels	ru_RU
dc.title	Sentiment in Academic Texts	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU