Language Sleuthing HOWTOor

Discovering Interesting Thingswith Python's

Natural Language Tool Kit

Brianna Laugher

modernthings.org brianna[@.]laugher.id.au

why?

Corpus linguistics on web texts

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

Like... mailing lists

luv-main as a corpus

√ Big collection of text x Messy data x Not annotated

what's interesting?

conversations

topics

change over time

(authors)

Step 1:

get the data

wget vs Python script

√ wget is purpose-built

√ convenient options like --convert-links

Meaningful URLs FTW

Sympa/MhonArc:

lists.luv.asn.au/wws/arc/luv-main/2009-04/

msg00057.html

Step 2:

clean the data

Cleaning for what?

Remove archive boilerplate

Remove HTML

Remove quoted text?

Remove signatures?

J.W.

J.W.

W.E.

Behind the scenes

J.W.

W.E.

what are we aiming for?

what do NLTK corpora look like?

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

Getting NLTK data...

an “NLTKism”

NLTK corpora types

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 ./.

Inaugural corpusA 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. ...............

But we still have lots of HTML...

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))

notice the blockquote!

>>> 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,[...]

What about blockquotes?

Step 3:

analyse the data

Getting it into NLTK

import nltkpath = 'path/to/files'corpus = nltk.corpus.PlaintextCorpusReader(path, '.*\.html')

What about our metadata?

Create a Python dictionary that maps filenames to categoriese.g.categories={}categories['2008-12/msg00226.html'] =

['year-2008', 'month-12', 'author-BM<bm@xxxxx>']

....etc

then...import nltkpath = 'path/to/files/'corpus = nltk.corpus.CategorizedPlaintextCorpusReader(path, '.*\.html', cat_map=categories)

Simple categories

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

...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 "%5d\t%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...)

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 distrosif w.lower().startswith(distro))

return cfd

popularcfd = getcfd(popular,4) # or 7 for monthspopularcfd.plot()nichecfd = getcfd(niche,4)nichecfd.plot()

another “NLTKism”

'Popular' distros by month

'Popular' distros by year

'Niche' distros by year

Random text generation

import randomwords = [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)

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

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

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 bufreadbusmaster speccyclecellx cellycheswick bellovincread clocalcurtail atldmcrs rscemdmmrbc dmostdmost dmcrs...

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 #YMMVtaglist = word_fd.items()ranges = getRanges(wordmin, wordmax)writeCloud(taglist, ranges, 'tags.html')

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

→

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...