A Comparative Analysis of Actors' and Actresses' Oscar Acceptance Speeches Based on Big Data Methodology

Liuchun Wen

2022

Abstract

The adopted methods and instruments include SPSS analysis, EXCEL statistics, python program, etc. Based on big data methodology, with the theoretical framework of Appraisal theory, the author makes a comparative analysis of the Appraisal resources in the actors’ and actresses’ Oscar acceptance speeches and their effects in realizing the Interpersonal function. The result of the study shows that both the Oscar acceptance speeches from actors and actresses share the same distribution feature of the Appraisal resources. The most frequently used ones are the Attitude resources, and the second are Graduation resources and the third ones are Engagement. The current study has broadened the application of the Appraisal theory. It proves that the Appraisal theory is applicable for the analysis of the field of Oscar acceptance speeches, the sub-genre of public speeches.

Paper Citation

in Harvard Style

Wen L. (2022). A Comparative Analysis of Actors' and Actresses' Oscar Acceptance Speeches Based on Big Data Methodology. In Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME; ISBN 978-989-758-630-9, SciTePress, pages 341-346. DOI: 10.5220/0011911700003613

in Bibtex Style

@conference{nmdme22,
author={Liuchun Wen},
title={A Comparative Analysis of Actors' and Actresses' Oscar Acceptance Speeches Based on Big Data Methodology},
booktitle={Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME},
year={2022},
pages={341-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011911700003613},
isbn={978-989-758-630-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME
TI - A Comparative Analysis of Actors' and Actresses' Oscar Acceptance Speeches Based on Big Data Methodology
SN - 978-989-758-630-9
AU - Wen L.
PY - 2022
SP - 341
EP - 346
DO - 10.5220/0011911700003613
PB - SciTePress