def ClusterTweetSim(self, cluster, tweet):
tweet_txt = tweet.text
min_similariy = 1.0
total_sim = 0
for index, row in cluster.data.iterrows():
curr_sim = Cosine_Sim.get_cosine(tweet_txt, row['tweet_text'])
if round(curr_sim, 2) >= 0.99: # duplicate tweet
return 1 # return once you find the duplicate
else:
total_sim = total_sim + curr_sim
#average similarity
min_similariy = (total_sim * 1.0 / len(cluster.data.index))
return round(min_similariy, 2)
