Sentiment and Sentence Similarity as Predictors of Integrated and Independent L2 Writing Performance

Autor/inn/en	Uzun, Kutay; Ulum, Ömer Gökhan
Titel	Sentiment and Sentence Similarity as Predictors of Integrated and Independent L2 Writing Performance
Quelle	In: Acuity: Journal of English Language Pedagogy, Literature and Culture, 7 (2022) 1, S.1-18 (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2541-0229
Schlagwörter	Natural Language Processing; Second Language Learning; Second Language Instruction; Writing (Composition); Prediction; Essays; Programming Languages; Computational Linguistics; Persuasive Discourse; Sentences; Multivariate Analysis; English (Second Language); Writing Evaluation + Suchen Sie Ihr Suchwort? Natürliche Sprache; Zweitsprachenerwerb; Fremdsprachenunterricht; Schreibübung; Vorhersage; Essay; Aufsatzunterricht; Linguistics; Computerlinguistik; Persuasion; Persuasive Kommunikation; Sentence analysis; Satzanalyse; Multivariate Analyse; English as second language; English; Second Language; Englisch als Zweitsprache
Abstract	This study aimed to utilize sentiment and sentence similarity analyses, two Natural Language Processing techniques, to see if and how well they could predict L2 Writing Performance in integrated and independent task conditions. The data sources were an integrated L2 writing corpus of 185 literary analysis essays and an independent L2 writing corpus of 500 argumentative essays, both of which were compiled in higher education contexts. Both essay groups were scored between 0 and 100. Two Python libraries, TextBlob and SpaCy, were used to generate sentiment and sentence similarity data. Using sentiment (polarity and subjectivity) and sentence similarity variables, regression models were built and 95% prediction intervals were compared for integrated and independent corpora. The results showed that integrated L2 writing performance could be predicted by subjectivity and sentence similarity. However, only subjectivity predicted independent L2 writing performance. The prediction interval of subjectivity for independent writing model was found to be narrower than the same interval for integrated writing. The results show that the sentiment and sentence similarity analysis algorithms can be used to generate complementary data to improve more complex multivariate L2 writing performance prediction models. (As Provided).
Anmerkungen	Acuity: Journal of English Language Pedagogy, Literature and Culture. Jl. Kolonel Masturi No. 288, Kec. Parongpong, Bandung Barat, 40559, Indonesia. e-mail: jurnal.acuity@unai.edu; Web site: https://jurnal.unai.edu/index.php/acuity/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01