Corpus Analysis is a semantic approach to analyzing a corpus – a set of methodically or arbitrary collected and electronically stored ‘real-life’ language samples such as speeches, magazine articles, and texting messages – with a goal to discern certain rules of language use, grammatical or lexical patterns, for instance, that are pertinent to a particular genre or type of text, serving as a valuable source for dialectology, sociolinguistics and other related fields. Corpora are investigated through the use of dedicated software. For example, softwares like Antconc, Google’s N-gram Viewer. Corpus analysis can be regarded as a sophisticated method of finding answers to the kinds of questions linguists have always asked. A large corpus can be a test bed for hypotheses and can be used to add a quantitative dimension to many linguistic studies. It is also true, however, that softwares do present us researchers with language in a form that is not normally encountered and that this can highlight patterning that often goes unnoticed. Corpus analysis has also therefore led to a reappraisal of what language is like. Corpus analysis comprises of terms such as Collocation, Concordance, Wildcards, Tokens and Lemmatization.
Collocation refers to a sequence or pattern of words that appear together or co-occur. Concordance represents a word or a phrase and its immediate context. In Corpus Analysis, concordance is used to analyse different use of a single word, word frequency and phrases or idioms. Tokens refer to an occurrence of an individual word which plays an important role in the so-called tokenization that involves the division of text or collection of words into tokens. This method is often used in the study of languages which do not delimit words with space. Lemmatization derives its form from the word lemma which refers to a set of different forms of a single word such as sight and sighted for example. Lemmatization refers to the process of grouping words that have the same meaning. Wildcards refer to special characters such as hyphens (-) or commas (,) which can represent a character or a word.
Here, we shall detail my application of various corpus analysis techniques on my chosen corpus of Violence in Video Games to investigate how violence maybe shaped across video games and to also check for any similarities or differences if any. Now we shall deal with the application of the techniques to the corpus under investigation here and what might be concluded from the results.
We shall begin with Collocations first on our study which is Violence in Video Games. As mentioned before Collocation is a key concept in corpus investigation, and one that will be examined closely in this essay, is that of collocation. Collocation refers to the regular and predictable co-occurrence of words in a text or an utterance. We characterise collocation as a ‘marriage contract between words’ and points out that the ‘marriage’ is stronger between some words than others.
One possible starting point in the study of collocation is the use of concordances, which have ‘been the major tool for accessing corpora’. Concordances are lines of text of a given length featuring the word under investigation (the node) in the centre of the page and its surrounding context (collocates) from which information can be drawn about patterns of relationships between words. The expanded context of concordance lines is key because, as it maybe noted that collocates are sometimes separated by intervening words.
20 minutes of playing a violent video game 'can cause people to become less Violent Video Games - ProCon.org.txt
settings and circumstances' that makes the game 'more likely to influence the player's 9.txt
if it is sanctioned within the game (e.g., killing children to gain energy). Game Studies - The “Moral Disengagement in Violent Videogames” Model.txt
ohemia Interactive, 2001), a first-person shooter game (FPS). All participants watched a cinematic Game Studies - The “Moral Disengagement in Violent Videogames” Model.txt
MiniGolf 3D), or a violent video game (Grand Theft Auto III or Grand Violent video games reduce teens' self-control, study shows.txt
of participants to play the same game (Halo II) but gave them different Violent video games make children more violent _ Centre for Educational Neuroscience.txt
to play either a non-violent video game (Pinball 3D or MiniGolf 3D), or Violent video games reduce teens' self-control, study shows.txt
take into account the type of game (violent vs. nonviolent) in the studies’ Metaanalysis of the relationship between violent video game play and physical aggression over time _ PNAS.txt
Minnesota passed the Minnesota Restricted Video Game Act, which made it illegal for Public policy and violence in video games _ ACM Interactions.txt
way to resolve con icts. Video game advocates contend that a majority of Violent Video Games - ProCon.org.txt
aggression after they played the video game against an unseen 'partner,' who actually Violent video games reduce teens' self-control, study shows.txt
appropriate test of the violent video game → aggression hypothesis: longitudinal designs tha Metaanalysis of the relationship between violent video game play and physical aggression over time _ PNAS.txt
notable critic of the violent video game aggression literature conducted studies in primar Children's violent video game play associated with increased physical aggressive behavior -- ScienceDaily.txt
esearchers uncovered concerning issues with video game aggression studies’ methodologies. Specifically, EFGamesandViolence.txt
'diagnosis': He could be in the game all day and all night. I Don't Blame Violent Video Games for Monday's Mass Shooting - The Atlantic.txt
the US Army released rst-person shooter game America's Army to recruit soldiers and Violent Video Games - ProCon.org.txt
violence in the context of a game and appropriate behavior in the real Violent Video Games - ProCon.org.txt
nine 80-minute sessions playing a video game and found that the games help 19 Violent Video Games Pros and Cons – Vittana.org.txt
8 * With regards to Postal 2 Demo Game and Postal 2 Share the Pain, the 9.txt
players control the action of the game and so become more 'immersed and 9.txt
A second method employed would be to identify collocations is the use of wordlists. One option is to use a collocation listing which offers counts of all instances (tokens) of collocates (types) within a given result. Another way is by presenting columns of collocates on either side of the node; one column for one place to the right, one column for two places to right, and so on up to six places to the right or left. It is suggested that this type of information is a particularly good starting point for the investigation of very frequent words, but warn that results need to be assessed with caution since they ‘suggest’ rather than ‘tell’ us things. A small extract of the results generated from the WordList tool from AntConc is attached below:-
#Word Types: 8441
#Word Tokens: 72191
#Search Hits: 0
3076 the
2270 of
1893 and
1753 to
1461 a
1368 in
1082 that
1025 video
1024 games
779 violent
634 is
614 on
611 for
518 s
462 game
458 as
442 violence
396 are
387 be
386 or
The wordlists in the corpus can be sorted in three ways: by frequency, by t-score, and by mutual information score. Each of these measures of the strength of a collocation has its own considerations.
Lemmatization is the grouping together of different forms of the same word. In search queries, lemmatization allows end users to query any version of a base word and get relevant results. Because search engine algorithms use lemmatization, the user is free to query any inflectional form of a word and get relevant results. For example, if the user queries the plural form of a word (routers), the search engine knows to also return relevant content that uses the singular form of the same word (router). Here below is a small extract of the results showing lemmatized form of the words from the Violence in Video Games Corpus.
#Lemma Types: 10957
#Lemma Tokens: 48494
1165 game game 393 games 772
1012 video video 1004 videos 8
725 violent violent 725
680 . . 680
458 ( ( 458
408 play play 177 played 75 playing 152 plays 4
309 , , 309
297 study studied 4 studies 153 study 137 studying 3
280 violence violence 280
247 ) ) 247
216 ). ). 216
196 may may 196
182 - - 182
177 & & 177
171 medium media 164 medium 7
165 aggression aggression 165
155 aggressive aggressive 155
154 effect effect 76 effected 1 effects 77
153 use use 70 used 38 uses 3 using 42
149 new new 146 newer 3
We can readily state that ‘the simplest way to identify collocate pairs is by their relative frequency’ which ‘can give a perception of the most common collocational associations’. The drawback here is that many high frequency collocates of a given node will also be high frequency words in general, and therefore likely to collocate frequently with many words other than the ones being examined simply by chance.
An alternative to straight frequency counts is to use what is known as a mutual information (MI) score. Mutual information calculates the appeal between two words, or the prospect that if one appears the other will appear in close presence to it. The corpus that we have chosen employs both positive and negative MI scores; the higher the score, the higher the mutual appeal between words. The major drawback of MI scores is that they tend to ‘compile uncommon words’ leading to highrankings for some uncommon combinations that may be exclusive to any particular corpus.
Another alternative measure of strength is t-score, which measures the likelihood of a word appearing as a collocate of one word rather than another. Unlike MI, t-score only measures the appeal of the collocate to the node and not vice versa, so lists tend to include many grammatical words which are important to the functioning of the node. The node, on the other hand, is not important to the functioning of the grammatical word.
No one of the above measures can be said to be to provide the best information about a particular collocate, so it may be best to use some or all of them in combination to ‘take advantage of the different perspectives provided’ in assessing the significance of a collocation.
The total number of tokens found in the corpus chosen is 72191. Further investigating, we draw out conclusions based on our results while trying to search for violence in video games.
Using concordance samples, one feasible starting point for corpus analysis is a random sample of concordance lines, which allow repeated collocation patterns to emerge. 20 line samples were attached for all our results. Genereally, concordance lines would select from ‘every nth example of the word’ entered in the initial query.
It is clear from the analysis presented here in this essay that corpus study has much to offer in revealing detailed information about the types and frequency of collocations of a given word/words in the Violence in Video Games corpus. There is reasonable evidence in the study that: violence is prevalent when people play violent games, which ultimately would lead to strong aggresion shown towards other people. What could be revealed in a deeper and larger-scale corpus study of the similarities and differences in checking for violence in video games is certainly much more than what was able to be revealed here. While my understanding of the similarities and differences has deepened somewhat, I remain uncertain of the actual usage of violence in video games and how drastic the results would be when larger corpus would be considered.
References
Kennedy, G. (1991) ‘Between and through: The company they keep and the functions they serve’. In Aijmer, K. and Altenberg, B. (Eds.) (1991) English Corpus Linguistics. Longman Group UK Limited.
Kennedy, G. (1998) An Introduction to Corpus Linguistics. Addison Wesley Longman Limited.
McCarthy, M. (1990) Vocabulary. Oxford University Press.
Sinclair, J. (1991) Corpus, concordance, collocation. Oxford University Press.
Sinclair, J. (Ed.) (1996) Collins COBUILD Learner’s Dictionary. HarperCollins Publishers Ltd.
Stubbs, M. (1996) Text and Corpus Analysis. Blackwell Publishers Ltd.