(1 - a) * (self.stats.get(term, (0, 0))[1] + a * delta ** 2))
def find_emerging_terms(self):
with open(os.path.join(self.result_dir,'burst_terms'), 'a', encoding = 'utf-8') as f:
f.write('Interval %d:\n' % self.ii)
for term, count in self.curr_term_count.items():
stats = self.stats.get(term,(0,0))
ewma = stats[0]
ewmvar = stats[1]
ewma = max(ewma, self.beta)
ratio = (count / self.curr_tweets_count - ewma) / (math.sqrt(ewmvar) + self.beta) #THE FORMULA
if ratio > self.s:
f.write('%s %f\r\n' % (term, ratio)) #\r\n under windows
f.write('\n')
f.flush()
if __name__ == '__main__':
ts = TwitterStream()
#datapath = '/Users/Adward/Github/Automatic-Rumor-Detection/TwitterEventDetection/TestData/original'
datapath = '/Volumes/Adward_Backup/SRTP/data'
dirlist = os.listdir(datapath)
for path in dirlist:
if path.startswith('201') and os.path.isdir(os.path.join(datapath,path)):
ts.source(os.path.join(datapath,path))
ts.sort()
temp = TemporalProcessor(3600,0.3,0.0002,8,
#result_dir='/Users/Adward/Github/Automatic-Rumor-Detection/TwitterEventDetection/TestData/serialized')
result_dir = '/Volumes/Adward_Backup/SRTP/serialized_test')
ed = EventDetector(TextProcessor(),temp,ts.generator())
ed.process_stream()
