1. Corpus Linguistics Richard Xiao lancsxiaoz@googlemail.com
Corpus analysis (2)
Corpus Linguistics
Richard Xiao

2. Outline of the session Lecture Practical Keyword Reference corpus
Key keyword
Practical
WST keyword
AntConc keyword
Wmatrix keyword / key concept
Extra: keyword analysis with CQPweb

3. What is a keyword?
Keywords are those words whose frequency is exceptionally high (positive keywords) or low (negative keywords) in comparison with a reference corpus
Keywords usually refer to positive keywords
But negative keywords are equally interesting (see Xiao and McEnery 2005)
They appear at the very end of your listing, in a different colour in WordSmith
They are omitted automatically from a keywords database for key keyword analysis and a keyword plot

4. Why keyword analysis?
Indicating the ‘aboutness’ (Scott 1999) of a particular text or corpus
Contents analysis, discourse analysis
Also revealing the salient features which are functionally related to a particular genre (Xiao and McEnery 2005)
Genre analysis, stylistic analysis

5. How to do keyword analysis
Make a wordlist of the target corpus
Locate or make a word list of a reference corpus
Scott (2005) “In search of a bad reference corpus”
The reference corpus is usually larger than the target corpus
The appropriateness of a reference corpus depends on your research questions!
Compare the frequency of each item in the two wordlists to extract keywords – done automatically
Analyse and interpret keywords – you will do it!

6. Keywords in the party speeches
Target corpus – just one text
David Cameron's speech at the Conservative conference (10 October 2012, Manchester)
Local copy available (David_speech Unicode text) - download and unzip the file into a file folder:
Reference corpus
The 100-million-word BNC: download and unzip (local copy available)
Tool
WST Keyword

7. Wordlist of David’s speech

8. Creating keyword list

9. Keyword extraction in progress
Warning: It can take time if you have loaded two large wordlists

10. Keywords in David’s speech
What do these
keywords tell us?
Negative keyword

11. Keyword: Plot view

12. What companies do keywords keep?

13. Why “marriage”?

14. Key clusters
Similar to word clusters,
but only keywords are used.

15. Key keywords
A key keyword is one which is "key" in more than one of a number of related texts
The more texts it is "key" in, the more "key key" it is
Can avoid extracting keywords which are unusually frequent in only a small number of files
Can be created automatically and as simple to extract as you do for keywords
n.b. Negative keywords are omitted automatically from a key keyword list

16. Making a batch wordlist
Specify a folder where you can write

17. Batch making keyword lists

18. Batch making keyword lists
Specify a folder where you can write

19. Making a KW database

20. Key keywords key coverage of the corpus
An "associate" is a keyword that appears in the same text

21. Keyword in AntConc
target corpus
reference corpus

22. Keyword in AntConc
Key words in David's speech (in relation to Ed's speech)

23. Wmatrix: Keywords and key concepts
POS and semantic tagging
Keyword / key concept analysis in Cameron’s speech in comparison with Miliband’s speech
Copy and paste the speeches into two separate text files
Save the two texts as David_speech.txt and Ed_speech.txt

24. Wmatrix: Keywords and key concepts
Login with your account using zhejiangxx account

25. Tagging Wizard

26. Tagging in progress

27. Tagging result

28. Labour frequency list

29. KWIC concordance

30. “My folders” Upload and tag Ed’s speech …and click on “My folders”
Warning: Your folder view may look different!

31. Open David_speech folder and select Ed_speech in “Keyword compared to” dropdown box

32. Keyword list to download!

33. Keyword cloud – even more interesting!

34. David’s key concepts (“Key concepts compared to”)

35. Keyword analysis in online corpora
Using Lancaster’s CQPweb to compare British English (LOB+FLOB) and American English (Brown + Frown)
Login CQPweb
Similar analysis can be done at BSFU’s CQPweb corpus hub (different corpora)
Account: ID=pass=test

36. Creating subcorpora

37. Creating subcorpus BrE

38. Creating subcorpus AmE

39. Making wordlists

40. Wordlist available now

41. Computing keywords
You can make adjustments to the statistical measure, cut-off point, and minimum frequency according your research purposes.

42. Keywords in BrE and AmE