opinion word 0.00329606
opinion target 0.00311189
opinion words 0.003017509
opinion analysis 0.002821644
same opinion 0.0026619729999999998
opinion mining 0.002589106
opinion targets 0.0025430369999999997
opinion expression 0.0024798399999999996
opinion identification 0.002475538
opinion subgroup 0.00245319
unsupervised opinion 0.0024394399999999998
opinion stances 0.0024098649999999998
opinion expressions 0.0023625649999999996
aids opinion 0.0023569619999999998
opinion dependencies 0.002351386
passive opinion 0.002350793
word target 0.00226431
opinion 0.00207182
word polarity 0.00202285
different sentences 0.001973156
positive word 0.001893484
other words 0.0018772659999999998
negative attitude 0.001798954
target extraction 0.0016860629999999998
attitude features 0.0016782379999999999
same target 0.001630223
word identification 0.0016279579999999999
discussion topic 0.0016179789999999999
different clustering 0.00159832
positive attitude 0.001573016
other work 0.0015592969999999999
word relatedness 0.001520824
word fine 0.001506135
opinon word 0.001504182
word opj 0.001504182
attitude vector 0.001500135
different posts 0.001474239
clustering algorithm 0.00146981
target identification 0.0014437879999999999
same topic 0.001434482
language model 0.001434359
sentiment features 0.001425656
model approach 0.001422811
different datasets 0.0014180470000000002
text clustering 0.001402585
sentiment analysis 0.001401014
english words 0.001374485
target form 0.001367661
classification model 0.0013461880000000001
different discussants 0.0013460610000000002
text mining 0.001345671
target trk 0.001343874
different types 0.001336511
analysis method 0.001335026
several methods 0.001332345
other algorithms 0.0013299689999999999
first dataset 0.001327118
other discussant 0.001326476
semantic analysis 0.001322311
different levels 0.001318518
first sentence 0.001315845
attitude prediction 0.001313357
different stages 0.0013053000000000001
individual words 0.0013017789999999999
similar text 0.0012998929999999999
discussant attitude 0.001298671
interaction features 0.001283004
posts text 0.001278504
seed words 0.001274178
spin model 0.0012719650000000001
text similarity 0.0012675359999999999
first method 0.00126428
baseline methods 0.001260553
english text 0.001257181
other discussants 0.001253518
such discussions 0.001252841
sentences figure 0.0012516250000000001
product features 0.001246282
attitude predictions 0.001245136
tion algorithm 0.001236216
attitude vectors 0.001233304
cue words 0.0012272070000000001
fies words 0.0012258759999999999
guage model 0.001224444
other studies 0.001224252
second dataset 0.001224147
other baselines 0.001219389
other languages 0.001215659
second sentence 0.001212874
discourse information 0.001211737
polarity prediction 0.001208195
walk model 0.001202505
polarity identification 0.0012023280000000001
discussion thread 0.001199353
topic question 0.001199346
attitude profile 0.001195484
attitude expressions 0.001194517
wikipedia data 0.0011916449999999999
attitude profiles 0.001189582
fine attitude 0.001185667
