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Transactions on Engineering and Computing Sciences - Vol. 11, No. 2

Publication Date: April 25, 2023

DOI:10.14738/tecs.112.14252.

Kang, N. (2023). The Korean Movie Minari: A Big Data Analysis. Transactions on Engineering and Computing Sciences, 11(2). 42-52.

Services for Science and Education – United Kingdom

The Korean Movie Minari: A Big Data Analysis

Namkil Kang

Far East University, South Korea

ABSTRACT

The ultimate goal of this paper is to analyze 42 articles of Google regarding the

Korean movie Minari. As a research tool for our goal, we used the software package

NetMiner. A major point to note is that one word has the highest frequency (1,247

tokens) and the highest proportion (57.8%). More specifically, it occurs 1,247 times

in 42 articles of Google. A further point to note is that topic 11 is the most occurred

one in 40 articles, followed by topic 3, topic 4, and topic 10, in that order. When it

comes to the use of keywords in 42 articles, the word film is the most widely used

one, followed by the word Minari, the word family, the name Jacob, and the name

Monica, in that order. Talking about degree centrality, the word family has the

highest degree centrality, followed by the name Monica, the word Minari, and the

word film, in that order. It is worthwhile noting, on the other hand, that the word

family has the highest closeness centrality since the distance among the word family

and the other words is the closest. Finally, this paper argues that the name Monica

has the highest Eigenvector centrality since it has the most neighbors. Thus, the

name Monica counts as the most influential and important.

Keywords: Minari, NetMiner, degree centrality, closeness centrality, Eigenvector

centrality

INTRODUCTION

The main purpose of this paper is to analyze 42 articles of Google written from December 2020

to May 2022 regarding the Korean movie Minari. We analyzed 42 articles of Google in terms of

the software package NetMiner. First, we aim to investigate the frequency, proportion, and

cumulative proportion of nouns such as common nouns and proper nouns. Also, we inquire into

the frequency, proportion, and cumulative proportion of nouns occurred in 42 articles of

Google. Second, we aim at observing 14 topics occurred in 42 articles of Google and 5 keywords

consisting of each topic. Also, we observe how often each topic turns up in 42 articles of Google.

Third, we attempt to consider how often major keywords constituting 42 articles of Google

occur. Fourth, we aim to examine degree centrality, closeness centrality, and Eigenvector

centrality and provide their maps. The term centrality refers to the so-called importance,

reputation, prominence, prestige, and influence. Through degree centrality, we can see core

words directly linked with their neighbors. The term degree refers to the number of directly

linked neighbors. On the other hand, closeness centrality indicates that the more the distance

between nodes is close, the more centrality is high. Finally, the term Eigenvector centrality

indicates that centrality is high if nodes are linked to nodes with high centrality. Put differently,

if my neighbors have many neighbors, my influence grows.

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Kang, N. (2023). The Korean Movie Minari: A Big Data Analysis. Transactions on Engineering and Computing Sciences, 11(2). 42-52

URL: http://dx.doi.org/10.14738/tecs.112.14252

RESULTS

Word Information

This section centers on investigating the frequency, proportion, and cumulative proportion of

nouns occurred in 42 articles of Google. Table 1 shows information on the frequency,

proportion, and cumulative proportion of nouns such as common nouns and proper nouns

occurred in 42 articles:

Table 1 Information on nouns

Value Frequency Proportion Cumulative Proportion

Common Noun 1607 0.745 0.745

Proper Noun 549 0.255 1

Total 2156 1

It is interesting to note that in 42 articles of Google, common nouns occur 1,607 times, whereas

proper nouns appear 549 times. It should also be pointed out that the use of common nouns is

almost three times higher than that of proper nouns. The proportion of common nouns is 0.745

(74.5%), whereas that of proper nouns is 0.255 (25.5%). As exemplified in Table 1, the total

number of common nouns and proper nouns is 2,156.

Now Table 2 shows information on the frequency, proportion, and cumulative proportion of

nouns occurred in 40 articles of Google:

Table 2 Word information

Value Frequency Proportion Cumulative

Proportion

1.0 1247 0.578 0.578

2.0 344 0.16 0.738

3.0 158 0.073 0.811

4.0 104 0.048 0.859

5.0 43 0.02 0.879

6.0 50 0.023 0.903

7.0 25 0.012 0.914

8.0 17 0.008 0.922

9.0 21 0.01 0.932

10.0 20 0.009 0.941

11.0 10 0.005 0.946

12.0 5 0.002 0.948

13.0 11 0.005 0.953

14.0 10 0.005 0.958

15.0 6 0.003 0.961

16.0 8 0.004 0.964

17.0 4 0.002 0.966

18.0 2 0.001 0.967

19.0 4 0.002 0.969

20.0 2 0.001 0.97