KFU. Card publication. Sentiment in Academic Texts

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

SENTIMENT IN ACADEMIC TEXTS

Form of presentation

Articles in international journals and collections

Year of publication

2019

Язык

английский

Authors, employees of KFU

Gafiyatova Elzara Vasilovna, author

Solnyshkina Marina Ivanovna, author

Solovev Valeriy Dmitrievich, author

Bibliographic description in the original language

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.

Annotation

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.

Keywords

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

The name of the journal

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

URL

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

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=204621&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Gafiyatova Elzara Vasilovna	ru_RU
dc.contributor.author	Solnyshkina Marina Ivanovna	ru_RU
dc.contributor.author	Solovev Valeriy Dmitrievich	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/eng/?p_id=204621&p_lang=2	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	Articles in international journals and collections	ru_RU