Shin (2021b). Sentiment Analysis ... for Scores of Persuasive Essays
작성자Dongkwang Shin작성시간21.06.30조회수96 목록 댓글 0Shin, D. (2021). Sentiment analysis as a predictor for scores of persuasive essays. Brain, Digital, & Learning, 11(2), 215-226.
Sentiment Analysis as a Predictor for Scores of Persuasive Essays
Dongkwang Shin Gwangju National University of Education
설득적 에세이 점수의 예측도구로서의 감성분석 기법
신동광 광주교육대학교
ABSTRACT
This study aimed to investigate to what extent sentiment analysis can be applied to tone analysis of persuasive essays as a predictor for scores of the essays. To this end, the sentiment analysis of the text mining program ‘Orange3’ was utilized. This study first looked at the effects of Vader's sentiment indices (positive, negative, neutral, and compound values) on the total score of positive and negative essays. As a result, in the positive essay, all the sentiment analysis results had a significant effect on the total score. In particular, it was confirmed that the compound value had the greatest influence as a predictor of the total score of the positive essay as well as the scores in the four sub-scoring domains—task performance, content, organization and language use. On the other hand, it turned out that there were no significant relationship between sentiment indices and scores of negative essays. These results suggest that even if learners write a persuasive essay with either positive or negative tone, they could expect a higher score when comparing the two opposite positions rather than the one-sided one. This was also confirmed to some extent through word cloud analysis. Of course, there were some limitations in this study as well but it showed the applicability of sentiment analysis to predict scores of persuasive essays.
Key words: Sentiment analysis, Orange3, persuasive essay, argument mining, word cloud