def handle(self, *args, **options):
# Get the configuration parameters
c = config.Config()
d = c.cfg
# Load the classifier
f = open('bayesclass.pickle')
classifier = pickle.load(f)
t = login()
KW = {'user' : d.get('api', 'user'),
'count' : d.get('api', 'count'),
'skip_users' : d.get('api', 'skip_users'),
'include_entities' : 'true',
'since_id' : 1,
'id' : 2}
p = Tweet.objects.aggregate(Max('tweet_id'))
latestId = p['tweet_id__max']
if latestId == None:
latestId = 1
KW['since_id'] = int(latestId)
api_call = getattr(t.statuses, 'user_timeline')
tweets = makeTwitterRequest(t, api_call, **KW)
print 'Fetched %i tweets' % len(tweets)
a = len(tweets)
for i in range(a):
txt = tweets[i]['text']
ref = tweets[i]['id']
src = tweets[i]['source']
outc = int(classifier.classify(word_feats(txt)))
created = mysql_date(tweets[i]['created_at'])
q = Tweet( datetime = created,
user = K['user'],
content = txt,
source = src,
tweet_id = ref,
prop = outc )
q.save()
f.close()
def handle(self, *args, **options):
n = 10 # Number of training tweets
SEARCH_TERM = ':)'
MAX_PAGES = 1
RESULTS_PER_PAGE = 1
LANGUAGE = "en"
INCLUDE_ENTITIES = "true"
KW = {
'domain': 'search.twitter.com',
'count': 1000,
'rpp': 100,
'q': SEARCH_TERM,
'lang': LANGUAGE,
'include_entities': INCLUDE_ENTITIES
}
t = twitter.Twitter(domain='search.twitter.com')
posfeats = []
for i in range(n):
tweets = makeTwitterRequest(t, t.search, **KW)
txt = tweets['results'][0]['text']
itemb = extractwords(txt)
posfeats.append((word_feats(itemb), '1'))
classifier = NaiveBayesClassifier.train(posfeats)
f = open('bayesclass.pickle', 'wb')
pickle.dump(classifier, f)
f.close()