>>> ludic = open('ludic.txt','r').read()

>>> ludic.starts.date = 06.13.09

>>> ludic.place = 'online' + 'LA' = ...all

>>> situationist.action = 06.20.09

>>> situationist.action.place = g727

>>> import nltk

>>> tokens = nltk.word_tokenize(ludic)

>>> text = nltk.Text(tokens)

>>> text.collocations()

Building collocations list

Situationist Ludic; enGage ludiCity; Ludic Drifts; Los Angeles;

Algorithmic Planner; Information Gadgetry; Situationist Algorithmic;

Situationist Information; Situationist Drifting; Situationist

Disruptive

>>> fdist = nltk.FreqDist(text)

>>> vocab = fdist.keys()

>>> vocab[:50]

['(', ')', 'the', ',', 'Situationist', 'of', 'and', '.', 'a', '\x95', 'to', 'Lud

ic', 'contributors', 'ludiCity', 'for', 'will', 'enGage', 'GC', 'in', '\x93', '\

x94', 'by', ':', 'is', 'at', 'all', 'SLDs', 'collective', 'ludus', 'on', 'their'

, 'Drifts', 'with', 'process', 'ludic', 'site', 'play', 'through', 'group', '2.'

, 'SAP', 'using', 'one', 'locus', 'set', 'constituo', 'individual', 'drifting',

';', 'Planner']

>>> text.dispersion_plot(["ludic","Situationist","play"])

ludic-plot.png

>>> tagged = nltk.tag.pos_tag(tokens)

>>> for (word,tag) in tagged:

... if tag=='VB': # verb, base form

... print word

...

interrogate

disrupt

bridge

seek

gather

be

operate

enGage

have

reconvene

play

generate

>>> tagged2 = nltk.tag.pos_tag(text.vocab().keys())

>>> for (word,tag) in tagged2:

... if tag[0]=='V':

... print '%s (%s)' % (word,tag)

...

enGage (VB)

is (VBZ)

collective (VBP)

ludens (VBZ)

maps (VBZ)

agreed (VBD)

relationships (VBZ)

equipped (VBD)

input (VBN)

means (VBZ)

occurring (VBG)

enGaged (VBD)

processed (VBD)

drinks (VBZ)

integrates (VBZ)

invites (VBZ)

remains (VBZ)

entails (VBZ)

website (VBP)

exploring (VBG)

be (VB)

have (VBP)

generated (VBD)

operate (VBP)

registering (VBG)

>>> fdist.plot(25)

ludic-plot2.png