def group_same(aspects):
for i in range(0, len(aspects)):
print i
aspects[i][0] = aspects[i][0].strip("#@")
token = aspects[i][0].rstrip("'s")
j = 0
nf = True
while j < i and nf:
if token.lower() in aspects[j][0].lower():
print("entered 1")
aspects[j][1] = aspects[j][1] + aspects[i][1]
aspects[i][1] = 0
nf = False
elif aspects[j][0].lower() in token.lower():
print("entered 1")
aspects[j][0] = token
aspects[j][1] = aspects[j][1] + aspects[i][1]
aspects[i][1] = 0
nf = False
else:
j = j + 1
aspects = sorted(aspects, key=lambda x: int(x[1]), reverse=True)
aspects = util.filter_rlist(aspects, 1, 1)
print aspects
return aspects
def group_same(aspects) :
for i in range(0,len(aspects)) :
print i
aspects[i][0]=aspects[i][0].strip("#@")
token=aspects[i][0].rstrip("'s")
j=0
nf=True
while j<i and nf :
if token.lower() in aspects[j][0].lower() :
print("entered 1")
aspects[j][1]=aspects[j][1]+aspects[i][1]
aspects[i][1]=0
nf=False
elif aspects[j][0].lower() in token.lower() :
print("entered 1")
aspects[j][0]=token
aspects[j][1]=aspects[j][1]+aspects[i][1]
aspects[i][1]=0
nf=False
else :
j=j+1
aspects=sorted(aspects,key=lambda x: int(x[1]),reverse=True)
aspects=util.filter_rlist(aspects,1,1)
print aspects
return aspects
def GreedyAspectRanking(outfile,tweets,topic,k) :
pos_tweets=tagger.runtagger_parse(tweets)
aspects_tweet=get_aspect(pos_tweets) # tweetwise aspects [[asp1,asp2],[],[asp1]]
"""
aspect_freq=ranking.get_freq(aspects_tweet)
aspect_freq=sorted(aspect_freq,key=lambda x: int(x[1]),reverse=True)
aspect_freq=error.correct(aspect_freq)
aspects_sel=util.filter_rlist(aspect_freq,10,1)
util.listTocsv(outfile1,aspects_sel)
aspects=util.listfromlist(aspects_sel,0)
#aspect_hits=ranking.pmi_list(aspects,topic,"results/pmi_"+topic+".csv")
"""
aspect_hits=util.csvTolist("results/pmi_"+topic+".csv")
aspect_hits=sorted(aspect_hits,key=lambda x: float(x[1]),reverse=True)
#util.listTocsv(outfile,aspect_hits)
asp_hits=util.filter_rlist(aspect_hits,6,1)
aspects1=util.listfromlist(asp_hits,0)
results=algo.GreedyNormal(outfile,aspects_tweet,aspects1,tweets,k)
return results
def GreedyAspectRanking(outfile, tweets, topic, k):
pos_tweets = tagger.runtagger_parse(tweets)
aspects_tweet = get_aspect(
pos_tweets) # tweetwise aspects [[asp1,asp2],[],[asp1]]
"""
aspect_freq=ranking.get_freq(aspects_tweet)
aspect_freq=sorted(aspect_freq,key=lambda x: int(x[1]),reverse=True)
aspect_freq=error.correct(aspect_freq)
aspects_sel=util.filter_rlist(aspect_freq,10,1)
util.listTocsv(outfile1,aspects_sel)
aspects=util.listfromlist(aspects_sel,0)
#aspect_hits=ranking.pmi_list(aspects,topic,"results/pmi_"+topic+".csv")
"""
aspect_hits = util.csvTolist("results/pmi_" + topic + ".csv")
aspect_hits = sorted(aspect_hits, key=lambda x: float(x[1]), reverse=True)
#util.listTocsv(outfile,aspect_hits)
asp_hits = util.filter_rlist(aspect_hits, 6, 1)
aspects1 = util.listfromlist(asp_hits, 0)
results = algo.GreedyNormal(outfile, aspects_tweet, aspects1, tweets, k)
return results
