polarity classification 0.00327151
polarity information 0.0028275089999999998
polarity word 0.002751628
training polarity 0.002734199
polarity classifier 0.002613188
polarity dataset 0.002517606
prior polarity 0.002491256
baseline polarity 0.002421114
strong polarity 0.002410028
contextual polarity 0.002385617
polarity values 0.0023551369999999998
review classification 0.00234313
polarity classifiers 0.002324924
polarity term 0.002322706
term polarity 0.002322706
polarity classi 0.002276966
polarity terms 0.002262747
own polarity 0.002241474
polarity informa 0.002237038
polarity classifi 0.002231111
sify polarity 0.002228921
sentiment classification 0.002100324
classification model 0.001982561
good features 0.001977726
polarity 0.00196774
dependency features 0.001943368
text classification 0.001936802
classification task 0.00192978
subjectivity classification 0.001920593
good classification 0.001832176
classification system 0.0018011900000000003
useful features 0.001778981
negative reviews 0.001778368
feature set 0.001757678
movie reviews 0.001725022
numerical features 0.0017235290000000001
jective features 0.001720393
negative review 0.001716338
informative features 0.0017147220000000001
ful features 0.001713186
classification systems 0.0016733310000000001
movie review 0.001662992
other words 0.001661312
rating information 0.00161147
such information 0.001610991
classification accuracies 0.0015852880000000002
larity classification 0.0015813700000000001
ity classification 0.001581107
good review 0.001567766
tivity classification 0.00156728
baseline feature 0.0015535839999999998
objective sentences 0.001508595
positive reviews 0.00150614
new feature 0.001496409
first problem 0.001487758
training data 0.00146484
negative word 0.001460866
same information 0.001457632
book reviews 0.001453879
features 0.00144932
training documents 0.001444673
positive review 0.00144411
feature lists 0.0014362479999999998
negative rating 0.001428679
feature space 0.0014277169999999998
training set 0.0014239270000000002
neutral reviews 0.001422417
review identification 0.001416807
objective information 0.001416451
feature selection 0.0014043159999999999
subjective information 0.001395013
automatic review 0.00138661
informative feature 0.001365612
ative reviews 0.001364335
augmented feature 0.001364127
feature selec 0.001362584
neutral review 0.001360387
dependency information 0.001353817
review identifier 0.001353057
negative results 0.001343069
review datasets 0.001310473
review identifi 0.001305446
review identifica 0.00130472
classification 0.00130377
text classifier 0.0012784799999999998
information extraction 0.001271442
svm classifier 0.001261349
document vector 0.00121557
first hypothesis 0.001210642
level sentiment 0.001204375
sentiment analysis 0.0012019420000000001
learning task 0.001178942
test documents 0.001167988
movie domain 0.001135204
other hand 0.001132747
trailer information 0.001123212
word lists 0.0011199259999999998
neutral sentiment 0.0011175809999999999
large number 0.001110834
first place 0.0011095810000000001
