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uberVU Hackaton - Noun-verb relationships
# more info:
from nltk import pos_tag, word_tokenize
import en # Nodebox English Linguistics library
import urllib, urllib2, re
import json
from time import time
def fetch_url(url, get=None, post=None):
user_agent = 'Andrei Olariu\'s Web Mining for Dummies'
headers = {'User-Agent': user_agent}
if get:
data = urllib.urlencode(get)
url = "%s?%s" % (url, data)
print url
req = urllib2.Request(url, post, headers)
response = urllib2.urlopen(req).read()
response = json.loads(response)
except Exception, e:
print 'error in reading %s: %s' % (url, e)
return None
return response
def get_tweets(values):
do a series of api calls at ubervu's api to get all
tweets matching the filtering options
url = ''
data = []
val = time()
while True:
response = fetch_url(url, values)
if not response or response['total'] == 0:
val = min([t['published'] for t in response['results']])
'until': val - 1,
return data
def tag_and_filter(text):
''' Takes a text, breaks the words apart, gets the POS tag for
each one of them, keeps only the nouns, verbs and adjectives
and puts them in the singular/present form
words = word_tokenize(text.lower())
filtered_words = []
i = 0
while i < len(words):
# filter RT, twitter names, hashtags, links
if words[i] in ('rt', '', '%'):
i += 1
elif words[i] in ('@', '#'):
i += 2
elif words[i] == 'http':
i += 3
word = re.findall(r'\w+', words[i])
if word:
i += 1
# Beware, nltk is pretty good at POS-ing, but very slow
# For better speed (but lower precision) use nodebox ling
pos_tags = pos_tag(filtered_words)
filtered = []
accepted = ['JJ', 'JJR', 'JJS', 'NN', 'NNP', 'NNPS', 'NNS', 'PRP', 'RB', \
'RBR', 'RBS', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']
# this is even better than stemming
for word, pos in pos_tags:
if pos in accepted:
if pos.startswith('NN'):
word = en.noun.singular(word)
elif pos.startswith('VB'):
word = en.verb.infinitive(word)
filtered.append((word, pos))
return filtered
def is_match(exp, text):
found = None
found =, text)
return found != None
def nouns_and_verbs(sentences):
Gets data like sentence = [('Jill', 'NNP'), ('Jack', 'NNP'),
('like', 'VB'), ('apples', 'NN'), ('oranges', 'NN')]
Returns data like ([('Jill', 'like'), ('Jack', 'like')],
[('like', 'apples'), ('like', 'oranges')])
def is_noun(word):
return is_match('NN(PS?|S)?', word)
def is_verb(word):
return is_match('VB(D|G|N|P|Z)?', word)
nv = []
vn = []
for s in sentences:
nouns_1 = []
verbs = []
nouns_2 = []
found_verb = False
for w in s:
if is_verb(w[1]):
found_verb = True
elif is_noun(w[1]):
if found_verb == False:
print w, ' not verb or noun'
for n in nouns_1:
for v in verbs:
nv.append((n, v))
for v in verbs:
for n in nouns_2:
vn.append((v, n))
return (nv, vn)
def update_model(model, sv_texts, vs_texts):
Receives a list of nouns_and_verbs and one of verbs_and_nouns
Updates a model
# 'who' stands for nouns, 'what' for verbs
# given a pair (a, b), an 'out' link will be created from a to b
# and an 'in' link from b to a (in case we want fast querying)
for pair in sv_texts:
who, what = pair
if not who or not what:
if who not in model['who']:
model['who'][who] = {'in': {}, 'out': {}}
if what not in model['who'][who]['out']:
model['who'][who]['out'][what] = 0
model['who'][who]['out'][what] += 1
if what not in model['what']:
model['what'][what] = {'in': {}, 'out': {}}
if who not in model['what'][what]['in']:
model['what'][what]['in'][who] = 0
model['what'][what]['in'][who] += 1
for pair in vs_texts:
what, who = pair
if not who or not what:
if who not in model['who']:
model['who'][who] = {'in': {}, 'out': {}}
if what not in model['who'][who]['in']:
model['who'][who]['in'][what] = 0
model['who'][who]['in'][what] += 1
if what not in model['what']:
model['what'][what] = {'in': {}, 'out': {}}
if who not in model['what'][what]['out']:
model['what'][what]['out'][who] = 0
model['what'][what]['out'][who] += 1
def get_links(model, word, word_type=None):
Queries the model for a word
The word_type (who/what) should be given, because some words
may appear in both categories
if not word_type:
if word in model['who']:
word_type = 'who'
if word in model['what']:
if word_type:
print 'word may be a verb or a noun, please specify'
return None
word_type = 'what'
if not word_type:
return None
threshold = 0.08
in_list = model[word_type][word]['in'].items()
in_list.sort(key=lambda x: -x[1])
total = sum([x[1] for x in in_list])
filtered = []
for element in in_list:
if element[1] > threshold * total:
filtered.append((element[0] + ' ' + word, element[1]))
out_list = model[word_type][word]['out'].items()
out_list.sort(key=lambda x: -x[1])
total = sum([x[1] for x in out_list])
for element in out_list:
if element[1] > threshold * total:
filtered.append((word + ' ' + element[0], element[1]))
return filtered
# get tweets
today = int(time() / 86400) * 86400
tweets = []
values = {
'format': 'json',
'count': 100,
'apikey': 'you\'l have to get your own apy key'
'since': today - 1 * 86400,
'until': today,
'q': 'iphone',
texts = [t['content'] for t in tweets]
# apply stemming, POS
texts2 = [tag_and_filter(t) for t in texts]
# parse sentences
nouns_verbs, verbs_nouns = nouns_and_verbs(texts2)
# create model
model = {
'who': {},
'what': {},
update_model(model, nouns_verbs, verbs_nouns)
# Get top pairs and check them out
results = []
for word in model['what'].iterkeys():
results.extend(get_links(model, word, 'what'))
results.sort(key=lambda x: -x[1])
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