The document provides a guide on utilizing Python's Natural Language Toolkit (NLTK) for corpus linguistics by discussing steps to acquire, clean, and analyze data from web texts. It outlines methods for data gathering using wget and Python scripts, data cleaning, and basic data analysis with frequency distributions for identifying popular topics and authors in email lists. Additionally, it mentions generating random text and visualizing word frequencies, supporting the notion that the web is a rich source of language data.

1. Language Sleuthing HOWTO
or
Discovering Interesting Things
with Python's
Natural Language Tool Kit
Brianna Laugher
modernthings.org
brianna[@.]laugher.id.au

2. Corpus linguistics on web
texts
why?

3. Because the web is full of
language data
Because linguistic techniques
can reveal unexpected insights
Because I don't want to have to
read everything

4. Like... mailing lists

5. luv-main as a corpus
√ Big collection of text
x Messy data
x Not annotated

6. what's interesting?
conversations
topics
change over time
(authors)

7. Step 1:
get the data

8. wget vs Python script
√ wget is purpose-built
√ convenient options like
--convert-links

9. Meaningful URLs FTW
Sympa/MhonArc:
lists.luv.asn.au/wws/arc/luv-main/
2009-04/
msg00057.html

11. Step 2:
clean the data

12. Cleaning for what?
Remove archive boilerplate
Remove HTML
Remove quoted text?
Remove signatures?

13. J.W.
J.W.
W.E.

14. Behind the scenes
J.W.
W.E.

15. what are we aiming for?
what do NLTK corpora look like?

16. Getting NLTK
sudo apt-get install python-nltk
in Ubuntu 10.04
or
sudo apt-get install python-pip
pip install nltk
or
from source at nltk.org/download

17. Getting NLTK data...
an “NLTKism”

19. NLTK corpora types

20. Brown corpus
A CategorizedTagged corpus:
Dear/jj Sirs/nns :/: Let/vb me/ppo begin/vb by/in
clearing/vbg up/in any/dti possible/jj
misconception/nn in/in your/pp$ minds/nns ,/,
wherever/wrb you/ppss are/ber ./.
The/at collective/nn by/in which/wdt I/ppss
address/vb you/ppo in/in the/at title/nn above/rb
is/bez neither/cc patronizing/vbg nor/cc jocose/jj
but/cc an/at exact/jj industrial/jj term/nn in/in
use/nn among/in professional/jj thieves/nns ./.

21. Inaugural corpus
A Plaintext corpus:
My fellow citizens:
I stand here today humbled by the task before us,
grateful for the trust you have bestowed, mindful
of the sacrifices borne by our ancestors. I thank
President Bush for his service to our nation, as
well as the generosity and cooperation he has
shown throughout this transition.
Forty-four Americans have now taken the
presidential oath. ...............

22. But we still have lots of HTML...

24. BeautifulSoup to the rescue
>>> from BeautifulSoup import BeautifulSoup as BS
>>> data = open(filename,'r').read()
>>> soup = BS(data)
>>> print 'n'.join(soup.findAll(text=True))

26. notice the blockquote!

27. What about blockquotes?
>>> bqs = s.findAll('blockquote')
>>> [bq.extract() for bq in bqs]
>>> print 'n'.join(s.findAll(text=True))
On 05/08/2007, at 12:05 PM, [...] wrote:
If u want it USB bootable, just burn the DSL boot disk to CD and fire it
up.  Then from the desktop after boot, right click and create the
bootable USB key yourself.  I havent actually done this myself (only
seen the option from the menu), but I am assuming it will be a fairly painless
process if you are happy with the stock image.  Would be interested in
how you go as I have to build 50 USB bootable DSL's in the next couple weeks.
Regards,
[...]

28. Step 3:
analyse the data

29. Getting it into NLTK
import nltk
path = 'path/to/files'
corpus = nltk.corpus.PlaintextCorpusReader(path,
'.*.html')

30. What about our metadata?
Create a Python dictionary that maps filenames to
categories
e.g.
categories={}
categories['2008-12/msg00226.html'] =
['year-2008',
'month-12',
'author-BM<bm@xxxxx>'
]
....etc
then...
import nltk
path = 'path/to/files/'
corpus =
nltk.corpus.CategorizedPlaintextCorpusReader(path,
'.*.html', cat_map=categories)

31. Simple categories
cats = corpus.categories()
authorcats=[c for c in cats if c.startswith('author')]
#>>> len(authorcats)
#608
yearcats=[c for c in cats if c.startswith('year')]
monthcats=[c for c in cats if c.startswith('month')]

32. ...who are the top posters?
posts = [(len(corpus.fileids(author)), author) for author in
authorcats]
posts.sort(reverse=True)
for count, author in posts[:10]:
print "%5dt%s" % (count, author)
→
1304 author-JW
1294 author-RC
1243 author-CS
1030 author-JH
868 author-DP
752 author-TWB
608 author-CS#2
556 author-TL
452 author-BM
412 author-RM
(email me if you're curious to know if you're on it...)

33. Frequency distributions
popular =['ubuntu','debian','fedora','arch']
niche = ['gentoo','suse','centos','redhat']
def getcfd(distros,limit):
cfd = nltk.ConditionalFreqDist(
(distro, fileid[:limit])
for fileid in corpus.fileids()
for w in corpus.words(fileid)
for distro in distros
if w.lower().startswith(distro))
return cfd
popularcfd = getcfd(popular,4) # or 7 for months
popularcfd.plot()
nichecfd = getcfd(niche,4)
nichecfd.plot()
another “NLTKism”

34. 'Popular' distros by month

35. 'Popular' distros by year

36. 'Niche' distros by year

37. Random text generation
import random
words = [w.lower() for w in corpus.words()]
bigrams = nltk.bigrams(words)
cfd = nltk.ConditionalFreqDist(bigrams)
def generate_model(cfdist, word, num=15):
for i in range(num):
print word,
words = list(cfdist[word])
word = random.choice(words)
text = [w.lower() for w in corpus.words()]
bigrams = nltk.bigrams(text)
cfd = nltk.ConditionalFreqDist(bigrams)
generate_model(cfd, 'hi', num=20)

38. hi...
hi allan : ages since apparently yum erased . attempts
now venturing into config run ip 10 431 ms 57
hi serg it illegal address entries must *, t close relative info
many families continue fi into modem and reinstalled
hi wen and amended :) imageshack does for grade service
please blame . warning issued an overall environment
consists in
hi folks i accidentally due cause excitingly stupid idiots ,
deletion flag on adding option ? branded ) mounting them
hi guys do composite required </ emulator in for
unattended has info to catalyse a dbus will see atz init3

39. hi from Peter...
text = [w.lower() for w in corpus.words(categories=
[c for c in authorcats if 'PeterL' in c])]
hi everyone , hence the database schema and that run on memberdb on mail
store is 12 . yep ,
hi anita , your favourite piece of cpu cycles , he was thinking i hear the middle
of failure .
hi anita , same vhost b internal ip / nine seem odd occasion i hazard . 25ghz
g4 ibook here
hi everyone , same ) on removes a "-- nicelevel nn " as intended . 00 . main
host basis
hi cameron , no biggie . candidates in to upgrade . ubuntu dom0 install if there
! now ). txt
hi cameron , attribution for 30 seconds , and runs out on linux to on www .
luv , these

40. interesting collocations
...or not
ext = [w.lower() for w in corpus.words() if w.isalpha()]
from nltk.collocations import *
bigram_measures = nltk.collocations.BigramAssocMeasures()
finder = BigramCollocationFinder.from_words(text)
finder.apply_freq_filter(3)
finder.nbest(bigram_measures.pmi, 10)
→
bufnewfile bufread
busmaster speccycle
cellx celly
cheswick bellovin
cread clocal
curtail atl
dmcrs rscem
dmmrbc dmost
dmost dmcrs
...

41. oblig tag cloud
stopwords =
nltk.corpus.stopwords.words('english')
words = [w.lower() for w in corpus.words()
if w.isalpha()]
words = [w for w in words if w not in stopwords]
word_fd = nltk.FreqDist(words)
wordmax = word_fd[word_fd.max()]
wordmin = 1000 #YMMV
taglist = word_fd.items()
ranges = getRanges(wordmin, wordmax)
writeCloud(taglist, ranges, 'tags.html')

43. another one for Peter :)
cats = [c for c in corpus.categories()
if 'PeterL' in c]
words=[w.lower() for w in corpus.words(categories=cats)
if w.isalpha()]
wordmin = 10
→

44. thanks!
for more corpus fun:
http://www.nltk.org/
The Book:
'Natural Language Processing
with Python',
2nd ed. pub. Jan 2010
These slides are © Brianna Laugher and are released under
the Creative Commons Attribution ShareAlike license,
v3.0 unported. The data set is not free, sadly...