def test_text_preprocessing(self):
text = "This is a sample text. # ! . "
analyser = TextAnalyser()
processed = analyser._preprocess(text)
expected = ('This is a sample text. # ! . ', ['sampl',
'text'], [('sampl', 1),
('text', 1)])
self.assertEqual(expected, processed)
def test_tokenization(self):
expected, sample_docs, objects = get_test_documents()
calculated = {}
analyser = TextAnalyser()
id = 0
for s in sample_docs:
d = analyser.add_document(s)
calculated[str(id)] = d
id += 1
self.assertEqual(expected, calculated)
def crawl(self, only_english=False):
'''
Performs the actual crawling.
'''
text_analyser = TextAnalyser(ngram=1, only_english=only_english)
exception_log = []
kw = otter.loadrc() # load api key
count = 0
while self.maxtime != self.to_date:
for page in range(PAGE_SIZE):
try:
search = otter.Resource('search', **kw)
#search(q='#jan25 OR #egypt OR #tahrir', mintime = time.mktime(mintime.timetuple()), maxtime = time.mktime(maxtime.timetuple()), type='tweet', offset=page*10)
search(q=self.keywords,
mintime=time.mktime(self.from_date.timetuple()),
maxtime=time.mktime(self.maxtime.timetuple()),
type='tweet',
perpage=100,
page=page + 1)
for item in search.response.list:
print "--------------------------------------------------------------------------"
print "Storing tweet #", count, "for the period", self.from_date, "until", self.maxtime
tt = self.type()
tt.url = item.url
analysed = text_analyser.add_document(item.content)
#if this tweet is really small just ignore it.
if len(analysed['tokens']) <= 3:
print "Ignoring this tweet"
continue
content = Content()
content.raw = analysed['raw']
content.tokens = analysed['tokens']
content.construct_word_freq_list(
analysed['word_frequencies'])
content.date = self.from_date
tt.content = content
tt.date = self.from_date
tt.retweet_count = item.trackback_total
tt.screen_name = item.trackback_author_nick
tt.author_screen_name = item.trackback_author_nick
tt.author_name = item.trackback_author_name
tt.save(safe=True)
if len(
Author.objects(screen_name=item.
trackback_author_nick)) == 0:
author = Author()
author.screen_name = item.trackback_author_nick
author.tweets.append(tt)
else:
author = Author.objects(
screen_name=item.trackback_author_nick).get()
author.tweets.append(tt)
author.save()
count += 1
except Exception, e:
print e
exception_log.append(e)
finally:
pass
def test_unicode_doc_translation(self):
expected, document = get_unicode_document()
analyser = TextAnalyser()
document = analyser.add_document(document)
self.assertEqual(expected, document["raw"])
Created on 1 Feb 2012
@author: george
'''
import unittest
from analysis.social import TwitterSocialAnalyser
from analysis.text import TextAnalyser
from collections import OrderedDict
tweet_with_RT = "RT @monaeltahawy: RT @Gheblawi Beyond belief: religious history & make-up of #Egypt interesting discussion #Copts http://www.bbc.co.uk/podcasts/series/belief"
tweet_with_VIA = "Breaking News - Messi spotted outside the Etihad #transferdeadlineday http://twitpic.com/8dwcum (via @AndrewBloch )"
not_a_retweet = "This is not a retweet #test"
tweet_with_almost_RT = "RT Beyond belief: religious history & make-up of #Egypt interesting discussion #Copts http://www.bbc.co.uk/podcasts/series/belief"
tweets = [tweet_with_RT, tweet_with_VIA, not_a_retweet, tweet_with_almost_RT]
t = TextAnalyser()
dataset = OrderedDict()
id = 0
for tweet in tweets:
d = t.add_document(tweet)
dataset[id] = d
id += 1
class Test(unittest.TestCase):
def test_retweet_filter(self):
tsa = TwitterSocialAnalyser(dataset)
result = tsa.filter_retweets()
expected = []
expected.append((0, t.add_document(tweet_with_RT)))
expected.append((1, t.add_document(tweet_with_VIA)))
