def _calcTFIDF(self):
tweets = self._api.user_timeline(user_id=self.id,
count=200,
include_rts=1)
tfidf = getTFIDFArray([t.text for t in tweets])
return tfidf
from tweeapi.IR import getTFIDFArray
from db import DBSingleton
import psycopg2.extras
import simplejson
from tweeapi import APISingleton
from tweeapi.utils import EvalUser
if __name__ == "__main__":
twitterApi = APISingleton.getInstance()
tweets = twitterApi.search(q=sys.argv[1].lower(), rpp=100, page=1, force=True)
tweets = [t.text for t in tweets]
tfidfArray = getTFIDFArray(tweets)
db = DBSingleton.getInstance()
cur = db.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute("select * from users where retweet_factor > 0.2 and impact_factor > 0.2 limit 1000;")
results = []
for row in cur:
try:
eUser = EvalUser.loadFromDBRow(row)
if eUser.getSim(tfidfArray) > 0:
results.append(eUser)
except Exception,e:
traceback.print_exc(sys.stdout)
pass
import simplejson
from tweeapi import APISingleton
from tweeapi.utils import EvalUser
if __name__ == "__main__":
twitterApi = APISingleton.getInstance()
tweets = twitterApi.search(q=sys.argv[1].lower(),
rpp=100,
page=1,
force=True)
tweets = [t.text for t in tweets]
tfidfArray = getTFIDFArray(tweets)
db = DBSingleton.getInstance()
cur = db.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(
"select * from users where retweet_factor > 0.2 and impact_factor > 0.2 limit 1000;"
)
results = []
for row in cur:
try:
eUser = EvalUser.loadFromDBRow(row)
if eUser.getSim(tfidfArray) > 0:
results.append(eUser)
except Exception, e:
traceback.print_exc(sys.stdout)
def _calcTFIDF(self):
tweets = self._api.user_timeline(user_id=self.id, count=200, include_rts=1)
tfidf = getTFIDFArray([t.text for t in tweets])
return tfidf
