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sentiment polarity 0.004471971
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many sentiment 0.00430481
twitter sentiment 0.0042449440000000005
sentiment information 0.004226904
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ing sentiment 0.00417001
sentiment score 0.004123975
sentiment classifier 0.004067474
sentiment lexicons 0.004057604
sentiment strength 0.004055396
sentiment classifiers 0.004041402
total sentiment 0.004006704
final sentiment 0.003996013
gold sentiment 0.00399512
current sentiment 0.003981377
annotated sentiment 0.003973574
last sentiment 0.0039634579999999996
sentiment signals 0.0039620820000000005
sentiment classifica 0.003938401
sentiment classifi 0.003937045
sentiment classi 0.003933788
sentiment clas 0.003928724
sentiment indicators 0.003928293
sentiment lexi 0.0039272330000000005
diverse sentiment 0.00392415
maximal sentiment 0.003923699
sentiment classier 0.003922397
conduct sentiment 0.003922397
notated sentiment 0.003922397
gooood sentiment 0.003922397
sentiment 0.00368301
classification model 0.003384
segmentation model 0.00294882
document model 0.002892292
joint model 0.002652133
tion model 0.002536904
model seg 0.002525593
ranking model 0.002524453
generation model 0.002465583
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classification features 0.00216468
classification feature 0.0019842700000000002
segmentation features 0.0017295000000000001
word vectors 0.0016539290000000002
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classification dataset 0.0016115910000000001
current word 0.0016037070000000002
text features 0.001601469
word order 0.001582796
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word embedding 0.0015653820000000001
word embeddings 0.001549224
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document vector 0.001502973
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sentence segmentation 0.001481723
classification datasets 0.001460261
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feature learning 0.001415019
different feature 0.001344578
training data 0.001344534
words table 0.0013233540000000001
segmentation results 0.001289273
specific features 0.0012798290000000001
nrc features 0.0012482980000000001
segmentation number 0.001231917
joint segmentation 0.0012301130000000001
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segmentation score 0.001204365
classification 0.00119858
segmentation candidate 0.001183568
vector space 0.001161565
test sentence 0.0011606099999999999
training set 0.001150694
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feature sets 0.001123118
feature representation 0.001122671
feature description 0.001116739
first work 0.001110243
segmentation annotation 0.001107712
segmentation ranking 0.0011024329999999999
gold polarity 0.001101071
segmentation result 0.001100254
other baseline 0.001098917
final feature 0.001098693
feature weights 0.001096512
timent polarity 0.001084432
polarity inconsistency 0.001068551
