A Comparative Analysis of Actors' and Actresses' Oscar Acceptance

Speeches Based on Big Data Methodology

Liuchun Wen

Guangdong Technical College of Water Resources and Electric Engineering, Guangzhou, China

Keywords: Big Data Methodology, Oscar Acceptance Speech, Python Program, A Comparative Analysis.

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.

1 INTRODUCTION

As is known to all, the representation of language

can be either in written form or in spoken form.

Brown and Yule list ten distinctive features "which

characterise spoken language"(1983:15). They point

out that the data of analysis of discourse should be

the language in use. The great differences between

spoken and written language result in great

distinctions between spoken and written discourse.

In Halliday’s Spoken and Written Language (1989),

he singled out the characteristics of spoken

discourse in terms of grammar, lexical, structure etc.

Generally speaking, spoken discourse tends to be

less informal than written discourse (Huang, 2001).

Halliday, the founder of Systemic Functional

Grammar (hereafter SFG), clearly states that his aim

of the construction of SFG is “to construct a

grammar for purposes of text analysis: one that

would make it possible to say sensible and useful

things about any text, spoken or written” (Huang,

2001: 29). He also explains that there are two levels

of discourse analysis: the lower level is to contribute

to the understanding of the text, and the higher one

is to the evaluation of the text. In this sense, the

application of SFG to the evaluative analysis of

spoken discourse is available.

According to Halliday’s SFG, language is

viewed as a way of making meaning and it has three

metafunctions: Ideational, Interpersonal and

Textual. As Chang (2004) points out, “In Halliday’s

model, the Interpersonal metafunction has to do

with how we use language to interact with other

people, to establish and maintain appropriate social

links with them, and to express our own attitude and

our evaluation of things or events in the world”.

Developed from SFG, Appraisal theory was set

up by Martin and his colleagues in the early 1990s

to extend the analysis of Interpersonal meaning from

a new perspective. There are three sub-systems of

Appraisal theory: Attitude, Engagement and

Graduation. Appraisal theory has been proved to be

a progress in discourse analysis as it has

incorporated the semantics of evaluation.

Appraisal theory has been effectively applied in

the analysis of different types of texts，including

spoken texts. There are, of course, many types of

spoken discourse. Huang (2001) states that in terms

of the form of discourse, spoken discourses include

everyday conversations, telephone talks, interviews,

public speeches, debates etc. So, public speech is

one important form among spoken discourses.

Public speeches are represented in various forms.

According to the field of public speech, it can be

divided into political speech, academic speech,

military speech, and so on. In recent years, many

Wen, L.

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

DOI: 10.5220/0011911700003613

In Proceedings of the 2nd International Conference on New Media Development and Modernized Education (NMDME 2022), pages 341-346

ISBN: 978-989-758-630-9

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

341

studies about political speeches have been carried

out from different linguistic perspectives. However,

few attempts have been made to the comparative

analysis of Oscar acceptance speeches (hereafter

OASs), a sub-genre of public speeches, within the

framework of Appraisal theory. This study will try

to fill this gap by employing the Appraisal theory as

the theoretical framework to make a comparative

analysis of the Appraisal resources in the actors’ and

actresses’ OASs and their effects in realizing the

Interpersonal function. We hope to find out if there

are any different Appraisal resources used by the

actors and actresses, although they have the same

goal to achieve, to express their strong feelings.

2 THE APPRAISAL ANALYSIS

OF THE OSCAR ACCEPTANCE

SPEECHES

the Appraisal theory is developed from the

Interpersonal metafunction within SFG. Ideational

metafuction, Interpersonal metafunction and Textual

metafunction are the three metafuctions.

we would analyse the ten OASs with the

theoretical framework of Appraisal theory. The

detailed analysis would be conducted from the

following four parts. The first part is the general

survey of the Appraisal resources in the actors’ and

actresses’ speeches. The second part to the fourth

part is to compare the actors’ and actresses’

speeches from the perspectives of the Attitude,

Engagement and Graduation systems and examine

the differences between them to explore the

underlying reasons.

2.1 The General Survey of the

Appraisal Resources in OASs

We have analysed the distribution of the Appraisal

resources in actors’ and actresses’ OASs, as is

shown in Table 1 and Table 2.

Table 1 The Appraisal Resources in Actors’ OASs

System Number Percentage (%) Ranking

Attitude 117 54.4 1

Engagement 22 10.2 3

Graduation 76 35.4 2

Appraisal 215 100

Table 2 The Appraisal Resources in Actresses’ OASs

System Number Percentage (%) Ranking

Attitude 93 44.7 1

Engagement 38 18.3 3

Graduation 77 37.0 2

Appraisal 208 100

Tables 1 and 2 show that in both the actors’ and

actresses’ OASs, there are almost the same amount

of Appraisal resources, 215 and 208. Both of them

have the most resources of Attitude in the three

subsystems and take up the biggest portion, up to

54.4% and 44.7%. The Graduation resources in

actors’ OASs are 76, taking up to 35.4%, ranking

the second, and in actresses’ OASs 77, 37.0%, and

also the second. The Engagement resources of both

are ranking the third, with 22 and 38, taking up the

percentages of 10.2% and 18.3%.

The statistics above indicates that both actors’

and actresses’ OASs employ three subsystems of the

Appraisal system, with the most abundant Attitude

resources. We would do further research on how the

three subsystems individually are involved in OASs.

Figure 1 Ideational, Interpersonal and Textual

metafunctions (Martin & White, 2008:8)

Figure 1 shows the interpersonal metafunction of

SFG. Ideational metafunction refers to the world

outside and inside of ourselves that we talk about,

presenting "our view of the world as consisting of

'going-on' (verbs) involving things (nouns) which

may have attributes (adjectives) and which go on

against background details of place, time, manner,

etc. (adverbials)" (Thompson, 2000: 76).

Interpersonal metafunction is concerned with

establishing and maintaining social relationships.

People can use languages to interact, to

communicate and to maintain and create social

relationships with each other. Textual metafunction

organizes the Ideational metafunction and

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Interpersonal metafunction into a coherent and

unified way in a text.

Martin and White further divide the Appraisal

theory into three subsystems: Attitude, Engagement

and Graduation. The overview of the Appraisal

resources is presented in Figure 2 (Martin & White,

2008: 38).

Figure 2 An Overview of Appraisal Resources

2.2 Analysis of Attitude in OASs

The potential goal of OASs is to let the winners

express their feelings that they are happy and

honored to accept the academic awards. Thereby,

we assume that most of the Attitude resources are

used to express their feelings and there are some

different ways of expressing that feelings between

actors and actresses. We have chosen five OASs

from actors and five from actresses. The analysis of

Attitude in this part is conducted by comparing the

employment of the three sub-systems of Attitude in

actors' and actresses' OASs.

In actors’ OASs, the general distribution of

Attitudinal resources is that Appreciation accounts

for 42.7%, ranking the first, and Affect 35.9%, the

second, and Judgement 21.4%, the third. In the five

OASs, 2 Affect, 3 Judgement and 1 Appreciation

are negative, while the rest of the Attitudinal

resources are positive. The negative resources are

listed as follows.

Affect

(1) For those who saw the signs of hatred as our

cars drove in tonight, I think that it is a good time

for those who voted for the ban against gay marriage

to sit and reflect and anticipate their great shame and

the shame in their grandchildren's eyes if they

continue that way of support. (M 3: 10)

Judgement

(2) I did not expect this, but I, and I want it to be

very clear that I do know how hard I make it to

appreciate me, often. (M 3: 3)

In Example (1), the actor uses the negative

Affect of "shame" to criticize those who voted for

the ban against gay marriage. The actor plays a

leading role in a movie about gay marriage, and by

this role, he wins the Oscar Actor in a Leading Role.

He loves the role he plays and supports the gay

marriage. By using the negative Affect of "shame",

he tries to align the potential audience who share the

same view and have the same positions.

In Examples (2), the actor makes negative

Judgement of himself to acknowledge others or

others’ supports.

We could generalize that although the actors use

all kinds of negative Attitudinal resources to express

their mixed and happy feelings at the time when

they receive the awards, all these resources can be

interpreted as positive ones.

the Affect resources in the five actresses' OASs

are 40, taking up to 43% in the three Attitudinal

resources, ranking the first. There are 29

Appreciation resources, with the proportion of

31.2%, which ranks the second. The least is

Judgement, with the number of 24, accounting or

25.8%. We could not find any negative Attitudinal

resources in the five actresses' OASs.

Figure 3 Judgement, Appreciation and Affect

By comparing the statistics in the two tables, we

note that there are two different figures in the two

different OASs.

The first one is that the ranking of the three

resources is different. In actors' OASs, the biggest

portion is the Appreciation resources, while in

actresses' OASs, the Affect resources occupy the

first place. It explains the fact that actors are more

rational than actresses when they receive awards and

make a speech on the stage. By employing

Appreciation than Affect resources, they are

expressing more feelings of assessment of objects,

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

343

artefacts, processes and states of affairs than their

own inner feelings of emotions.

The second one is the distribution of negative

Attitudinal resources. There are several negative

resources in actors' OASs, while we could not find

any negative resources in actresses' OASs. It reflects

that actors are showing greater sense of humour than

actresses on the awarding-received stage. Because

all the negative resources can be interpreted as

positive ones. The negative usage of languages

reversely is one way of showing humour.

2.3 Analysis of Affect in OASs

Affect deals with utterances "which either convey(s)

a negative or positive assessment" or invite "the

reader to supply their own negative assessment"

(White, 2001:1). What is valued is the speakers' or

writers' emotional states. There are four sub-

categories in Affect, which are un/happiness,

in/security, dis/inclination and dis/satisfaction, as is

illustrated in Table 3. In this part, the author would

examine the distribution of the four categories in

both of the OASs.

Table 3 Distribution of Affect in Actors' OASs

Subsyste

Un/ha

ppines

In/securi

Dis/satis

faction

Dis/incl

ination

Total

number

38 0 3 1

Percenta

ge(%)

90 0 7.5 2.5

+/- + - + - + - + -

Total

number

0 0 0 1 2 1 0

Percenta

ge(%)

0 0 0

Un/happiness, among the four categories, take

up the biggest proportion of 90%. There are thirty

eight out of forty two. All of them are positive

emotional feelings - happiness. In the five OASs,

the words which are used to express the authors'

gratitude should be pointed out particularly, in the

form of noun, verb, adjective or adverbial, such as

acknowledgement, thankful. In OASs, the speakers

try to align the potential audience and invoke the

audience's positive value toward the winners and the

ones who have helped them. The speakers also try to

align the ones who have helped the speakers by

mentioning their names for their contributions. For a

better understanding, we will list some examples to

be focused on.

(3) And finally, I want to thank my mom and my

dad; I want to thank my wife Keisha, my children,

my ancestors who continue to guide my steps, and

God, God who believes in us all and who's given

me this moment in this lifetime that I will hopefully

carry to the end of my lifetime into the next

lifetime. (M 1: 13)

(4) I wrote something down because I thought if

it would happen I would be a little overwhelmed

and I am. (M 1: 4)

The examples above demonstrate that the word

"thank" here is interpreted that the speakers like or

love someone so much that the speakers want to

express their positive emotion to the ones from

whom they get help or support. So the words of

"thank" are put into the category of un/happiness. It

shows their emotions of happiness. In the five

OASs, there are thirty positive words of "thank",

which are 78.9% of the un/happiness category.

there are three dis/satisfaction, with one positive

and two negative. There is one Dis/inclination,

which is positive. Dis/satisfaction deals with "our

feelings of achievement and frustration in relation to

the activities in which we are engaged" (Martin &

White, 2008: 50). Dis/inclination is irrealis Affect,

in which there is a Trigger.

(5) I'm very, very proud to live in a country that

is willing to elect an elegant man president and a

country who, for all its toughness, creates

courageous artists, and this is in great due respect

to all the nominees, but courageous artists who,

despite a sensitivity that sometimes has brought

enormous challenge, Mickey Rourke rises again and

he is my brother. (M 3: 13)

(6) For those who saw the signs of hatred as our

cars drove in tonight, I think that it is a good time

for those who voted for the ban against gay marriage

to sit and reflect and anticipate their great shame

and the shame in their grandchildren's eyes if they

continue that way of support. (M 3: 10)

Example (5) is the positive dis/satisfaction. By

expressing the feelings "proud" in relation to the

activity of being "in a country that is willing to elect

an elegant man president and a country who, for all

its toughness, creates courageous artists", the

speaker tries to align as many potential audience

who share the same view and take the same position

as possible.

By adopting the negative resources of

Dis/satisfaction in Example (6), the speaker tries to

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be distant from those who voted for the ban against

gay marriage.

2.4 Analysis of Graduation in OASs

As we introduced before in Chapter 3.3.3, the

system of Graduation deals with the gradability of

the other two systems of Appraisal theory: Attitude

and Engagement. Under Graduation, there are two

sub-systems, Force and Focus. In this chapter, we

are going to conduct a detailed analysis of the

distribution of these two sub-systems. We tend to

examine how the Graduation resources are used to

valuate the turning up or down Oscar winners'

positive or negative attitudinal feelings to intensify

the degree of their utterance. The general

distribution of the Graduation resources in actors'

and actresses' OASs are seen in Tables 4 and 5.

Table 4: The General Distribution of Graduation Resources in Actors' OASs

Force

Focus Up-scale

Down-

scale

Total

Intensifi-

cation

Quantifi-

cation

M 1 6 4 3 11 2 13

M 2 10 0 1 11 0 11

M 3 11 5 2 17 1 18

M 4 17 4 1 22 0 22

M 5 16 5 1 19 3 22

Total 50 18 8 70 6 76

Percentage 65.8% 23.7% 10.5% 92.1% 7.9% 100%

Table 5: The General Distribution of Graduation Resources in Actresses' OASs

Force

Focus Up-scale

Down-

scale

Total

Intensifi-

cation

Quantifi-

cation

Pr. 1 11 4 0 15 0 15

Pr. 2 5 0 2 7 0 7

Pr. 3 13 1 4 18 0 18

Pr. 4 9 6 1 16 0 16

Pr. 5 12 8 1 21 0 21

Total 50 19 8 77 0 77

Percentage 64.9% 24.7% 10.4% 100% 0 100%

From Tables 4 and 5, we could see that both of

the OASs have more Force resources than Focus

resources, and within Force resources,

Intensification accounts for more than

Quantification. Most of the resources are to up scale

the Attitude and Engagement resources.

3 CONCLUSION

In our research, we have selected ten pieces of

OASs of the recent years, five by leading actresses

and five by leading actors. In this thesis, because of

space limitation, we choose four pieces of OAS and

elaborate them. All of the pieces of OASs are

downloaded from the Internet through the homepage

of The Academy: http://www.oscars.org. Within the

framework of the Appraisal theory, through

qualitative approach, we have conducted a detailed

analysis of the four samples, trying to find out the

Interpersonal functions of OASs.

For the analysis of the Attitude resources in the

ten OASs, we note that there are two different

figures in the two different OASs.

The first one is that the ranking of the three

resources is different. In actors' OASs, the biggest

portion is the Appreciation resources, while in

actresses' OASs, the Affect resources occupy the

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

345

first place. It explains the fact that actors are more

rational than actresses when they receive awards and

make a speech on the stage. They employ more

Appreciation than Affect to express more feelings of

assessment of objects, artefacts, processes and states

of affairs than their own inner feelings of emotions.

The second one is the distribution of negative

Attitudinal resources. There are several negative

resources in actors' OASs, while we could not find

any negative resources in actresses' OASs. It reflects

that actors are showing greater sense of humour than

actresses on the awarding-received stage. Because

all the negative resources can be interpreted as

positive ones. The negative usage of languages

reversely is one way of showing humour.

The Engagement resources employed in the

actors' and actresses' OASs are different. From

Table 4.10, it is evident that in actors' OASs, the

Dialogistic contraction and Dialogistic expansion

share almost the same proportion of Engagement

sources. Within the Dialogistic contraction, Deny

holds larger shares than other resources, and we

cannot find any Concur resources. In Dialogistic

expansion, all of the resources are Entertain, no

Attribute can be found. It shows that the actors pay

more attention to interaction with the audience as

well as to expressing his feelings.

The research on the Graduation resources clearly

shows that both of the OASs have more Force

resources than Focus resources, and within Force

resources, Intensification accounts for more than

Quantification. Most of the resources are to up scale

the Attitude and Engagement resources.

ACKNOWLEDGMENTS

The study is funded by 2021 Education and

Teaching Innovative Research Program in

Guangdong Technical College of Water Resources

and Electric Engineering: The Study of the Model of

Vocabulary Teaching in College English Based on

the Theory of Lexical Chunks (GX0205JGXM007)

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