IDENTIFYING MOOD SYSTEM AND THE QUALITY OF SPEECH ON TAYLOR SWIFT’S SPEECH AT THE GRADUATION OF NEWYORK UNIVERSITY 2022

ARAMI, AZURA (2023) IDENTIFYING MOOD SYSTEM AND THE QUALITY OF SPEECH ON TAYLOR SWIFT’S SPEECH AT THE GRADUATION OF NEWYORK UNIVERSITY 2022. S1 thesis, Universitas Mataram.

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Abstract

This study examines mood systems in Taylor Swift‟s speech at the graduation of Newyork University in 2022. This research aims to identify the types of mood systems and the most dominant one based on the theory by M.A.K Halliday in SFL, and describe the role of mood systems to the quality of speech. The data were interpreted by using the mixed-method approach with an explanatory design. The source of data is a youtube video of Taylor Swift‟s speech that has 23.40 minutes. The researcher discovered there were 201 clauses in Taylor Swift‟s speech. The findings indicate that all types of mood systems appeared in the speech of Taylor Swift. The most dominant mood type is declarative mood types with a percentage 91.04% and followed by imperative mood type with a percentage 5.47% and the least mood type is interrogative mood type with a percentage only 3.48%. Whereas, the most dominant modality is a high-degree modality with a percentage 58.70%, and the second the most dominant modality is a median-degree modality, and the least modality appeared in the speech is lowdegree modality with the percentage 2.48%. Additionally, the role of mood systems in the quality of speech is essential since from the mood system the researcher assumes that the speech by Taylor Swift is a quality speech based on the mood system analysis.

Item Type: Thesis (S1)
Keywords (Kata Kunci): SFL, mood system, mood types, modality, Taylor Swift’ speech
Subjects: L Education > LC Special aspects of education
L Education > LC Special aspects of education > LC5201 Education extension. Adult education. Continuing education
Divisions: Fakultas Keguruan dan Ilmu Pendidikan
Depositing User: Meike Megawati
Date Deposited: 07 Nov 2023 07:48
Last Modified: 07 Nov 2023 07:48
URI: http://eprints.unram.ac.id/id/eprint/43143

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