Corpus Analysis on Violence in Video Games

3676 words (15 pages) Essay in Psychology

08/02/20 Psychology Reference this

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Introduction:-

Through corpus analysis, being a semantic approach to analysing a corpus which is a set of methodically or arbitrary collected and electronically stored real-life language samples such as speeches, magazine articles and/or texting messages.

The main goal of corpus analysis is to detect certain decree of language use, grammatical or lexical patterns, for example, that are pertinent to a particular genre or type of text. This serves as a valuable source for dialectology, sociolinguistics and other related fields.

In this essay, I aim to exhibit through the application of corpus analysis along with the help of discourse analysis in knowing whether violence in video games can or may lead to being violent and/or aggressive in real life while in a way maybe showcasing the same violent/aggressive nature while playing video games.

In order to this, corpus analysis will be carried out on a set of at least 20 files being proposed as our use case, in this case, our main corpus in knowing and researching about violence in video games.

Corpus analysis comprises of terms such as collocation, concordance, tokens, stoplists, lemmatization, clusters/n-grams, keyword lists and wordlists.

I shall dig deep into these concepts and apply them individually to our corpus and investigate how violence maybe shaped across video games and to also check for any similarities or differences if any.

Also, what I might have achieved from the results, I shall delve into the utilization of the procedures to the corpus that we need to inspect. For my study of corpus analysis, I shall use Antconc software to help me in drawing conclusions about the aforementioned terms.

Corpus Methodology:-

 

Wordlist:-

After attaching the files in Antconc and hitting the Word List tool, the number of word types are 7645. Word types refer to the count of each word form. We also see the number of word tokens turn out to be 62146. Word tokens refer to the total count of each instance of each word. On checking the results, we see that different forms of the word are counted as different word types, eg ‘game’ and ‘games’ are different word types and are counted separately in the Word List. Attaching a small extract of 20 lines showing the list of words and their frequency.

Word

Frequency

the

2650

of

1994

and

1638

to

1540

a

1248

in

1165

that

930

video

849

games

843

violent

649

on

538

is

530

for

523

s

414

as

405

game

403

violence

385

be

352

are

350

or

349

Tokenization:-

Next, we shall dive into the concept of Tokenization. Tokenization is considered to be a process of delimiting and conceivably dividing sections of a text into words. After applying the Punctuation setting in the Token Definition option in Antconc to test for tokenization and thereafter running the search again, we see that the number of word types and word tokens have changed. Word types being 11330 and word tokens being 63791. Attaching a small extract of 20 lines showing the new list of words and their frequency.

Word

Frequency

the

2621

of

1990

and

1607

to

1529

in

1150

a

1144

that

903

video

831

.

656

games

632

violent

605

on

524

is

520

for

516

(

456

as

401

are

349

be

344

game

344

or

337

From the above table and after applying the process of tokenization, we see that the punctuations like periods (.) and parentheses [()] are considered as separate words.

Stoplists:-

Next, we shall check how to use stoplists in order to remove certain words. For this process, we shall use the Natural Language Tool Kit Python library text file to help with the same.

After applying the stoplist to our corpus, the number of word types is 11200 and the number of word tokens is 39467. In the stoplist we have also added punctuation marks and special characters that needs to be removed when we run the analysis on the given corpus. In the result, we only see a list of grammatical words unlike when we saw words like ‘the’, ‘and’ in when no filter was applied. Also, we have added punctuation marks to the stoplist file in order to remove them while running the analysis on the corpus. Showing the list below of 20 line results after attaching the stoplist text file.

Word

Frequency

video

831

games

632

violent

605

game

344

violence

243

may

181

play

159

aggression

148

media

141

aggressive

132

studies

131

moral

128

new

128

playing

122

research

121

people

117

found

113

study

109

social

108

also

103

Lemmatization:-

The next concept that I would be going over involved in corpus analysis is called lemmatization.

Combining together different forms of the same word is called lemmatization. To query any version of a base word and get relevant results; lemmatization helps us to do this. For eg, ‘turn’ is the lemma for turns, turning, turned. These words are inflections for the word ‘turn’.

Normally, search engines would prefer using lemmatization as any articulations forms of any words would give us admissible results.

Attaching a 20 lines extract of the result below showing various lemma forms.

Lemma Word Forms

Lemma

Frequency

game 344 games 632

game

976

video 831 videos 7

video

838

violent 605

violent

605

play 159 played 59 playing 122 plays 4

play

344

studied 3 studies 131 study 109 studying 3

study

246

violence 243

violence

243

may 181

may

181

aggression 148

aggression

148

media 141 medium 6

medium

147

effect 71 effected 1 effects 74

effect

146

aggressive 132

aggressive

132

use 58 used 33 uses 3 using 37

use

131

new 128 newer 2

new

130

moral 128

moral

128

research 121 researched 1 researches 1

research

123

child 30 children 87

child

117

people 117

people

117

found 113 founded 1

found

114

link 56 linked 18 linking 10 links 29

link

113

social 108

social

108

While taking out grammatical or function words like ‘to’, ‘from’, ‘my’, ‘they’, essentially the sentences do make complete sense even after taking out words like prepositions, pronouns, etc. However, when we do remove them, some of the sentences might become meaningless or pointless.

For eg, ‘I want to play video games with Simon.’

By taking out the prepositional words from the above sentence, the sentence now becomes:

‘I want.’

This is still grammatically sound, but pretty much pointless or meaningless.

Concordances:-

The concordance is a way to view words in context. Word frequencies are useful, but without context we risk drawing false conclusions (https://pdfs.semanticscholar.org/fa9a/84ae6215251b879569c4f902e14d71fb49f9.pdf).

Lines of text of a given sentence featuring the word/words under investigation are in the centre of the page, namely in this case checking for concordances for words like “violence”, “video”, “games” and the neighbouring context known as collocates in those same sentences from which we could draw about arrangements of links between the stated words.

When searching through the results for ‘game’, we get 344 hits.

On applying wildcard character ‘*’ along with ‘game’ such as ‘game*’, the number of hits are 1307.

On searching for ‘violence’ in the concordances tool, and while adjusting the sorting levels such as First (1L), Second (1R), Third (2R), we see that most of the words 1L of ‘violence’ appear to be mostly prepositions, conjunctions and determiners.

For, eg – words like ‘about’, ‘and’, ‘between’.

Sentence

Grammatical Part of Speech

tended to have more fantasies about violence and to think violence in real

about – preposition

the debates over video game-playing and violence appear to be coming back.

and – conjunction

of a violent outcome. Not all violence is the result of violent game

all – determiner

Words appearing to 1R of ‘violence’ are mostly verbs and nouns.

Sentence

Grammatical Part of Speech

does the ubiquity of video game violence beget real-life violence?

beget – verb

frequently embed cues that effectively frame violence enacted against seemingly social beings as

enacted – verb

ESSENTIAL FACTS ABOUT Games and Violence Facts, common sense, and numerous studies

Facts – noun

When fixing the sort levels as mentioned before while searching for ‘violence’ to look for concordances, we see that 1R to violence were verbs and nouns, the sentence did sound in perfect context and were meaningful.

When the sort levels are changed further, we see that words appear to be out of context thus deeming the entire sentence meaningless.

Thus, ‘violence’ along with the changing sort levels makes and breaks sentences in and out of context.

When searching for ‘study*’, we see that the number of concordance hits are 134. The results show different forms of the word ‘study’ such as “studying”, “study’s” wherein the sentences containing these words make perfect sense.

For eg, particularly problematic in the context of studying antisocial behaviours, such as aggression

subsequent increases in physical aggression,” the study’s lead author Jay Hull said in

Collocations:-

Collocations are an arrangement of words or terms that follow more generally than would be predicted by chance (https://www.jstor.org/stable/27919940?seq=1#page_scan_tab_contents).

The understanding to use a particular language in this case, English, comprises of a recognition of a peculiar feature of the language known as collocation.

Collocation is that practice of the language whereby two or more words work together in a certain speech or writing.

On changing the window span from 1L to 1R and minimum collocate frequency to 3, keeping the collocate measure set to MI and searching for the term ‘violence’, the total number of collocate types obtained is 32 and total number of collocate tokens is 331.

We see various combinations of words and types of collocations. Namely, there would a noun and noun combination, preposition and noun combinations.

Listing some of the examples below:

Noun and noun combination when ‘violence’ is searched:

Sentence

Grammatical Part of Speech

might frequently embed cues that effectively frame violence enacted against seemingly social beings

frame violence – noun + noun

for the same reasoning as between violence and real-life crime

between violence – preposition + noun

Clusters/N-Grams:-

The Clusters tool is a much better enhancement to the Collocation tool in Antconc. It is often a much better of showing sorted groups of co-occurring words.

When we search for ‘violent’ in the Clusters tool in Antconc with cluster size set minimum to 2 and maximum to 3, we see combinations of words such  as ‘violent video’, ‘violent crime’,

‘violent media’, ‘violent video games’, ‘violent behaviour’ , etc.

Attaching a small output of the result showing the frequency and range of the above mentioned clusters of words.

Cluster

Range

Frequency

violent video

23

341

violent crime

6

14

violent media

9

19

violent video games

23

200

violent behaviour

3

4

On checking for N-Grams through Antconc, we see the total number of n-gram types to be 95085 and tokens to be 127507.

Most noticeable N-Grams are ‘violent video games’.

Keywords:-

Keywords are words that occur in a particular speech or text more often that you would normally expect (https://ota.ox.ac.uk/documents/searching/handbook.html).

Keywords are calculated based on statistical tests which generally compare word frequencies in a speech or text against their predicted frequencies that are derived from a much larger corpus which we would use a reference for normal language usage.

Keyness would then be the trait a word or a given phrase has of being “key” in context.

After having attached the BNC_Wordlist file in Antconc and running the Keyword List tool, we see words sorted out based on their keyness and their frequency.

Words that show up in the top 20 results namely are ‘violent’, ‘game’, ‘aggression’, ‘video’.

Attaching an output of the result showing the keyness and their frequency of the above mentioned words.

Keyword

Keyness

Frequency

violent

+ 5646.03

605

game

+ 1784.01

344

aggression

+ 1196.19

148

video

+ 6985.15

831

With a high keyness score, it could be assumed in a way that playing violent games can often lead to violent aggression in our day-to-day lives.

Having chosen ‘violent’ from the keyword results list and checking them over in the concordance tool, we see the number of hits to be 605. Also, we have set the sort level to 1L, 2L and 3L, we see collection of words wherein ‘association’ is being connected to violence. Also, words such as ‘disengagement’, ‘embedded’, ‘link’ are in a way show strong associations with the word violence.

Conclusion:-

By concluding this essay, we can see that this corpus that I have chosen to study can offer heaps in revealing definite and distinct information about certain categories and recurrence of collocations of a given word/words in my chosen corpus which is violence in video games.

What could be affirmed in a broader and greater-scale corpus study of the closeness and distinctions in checking for violence in video gamesis certainly more than what was able to be stated here.

While my understanding of the closeness and distinctions has expanded considerably, I remain uncertain of the actual usage of violence in video games and how drastic the results would be when a larger corpus would be considered.

References:-

 

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