domain data 0.00243947
sentiment classification 0.002126863
training data 0.0020064230000000002
labeled data 0.001973528
data set 0.001908764
test data 0.0018541730000000002
sentiment scores 0.001734357
sentiment score 0.001726057
similarity matrix 0.0017240060000000001
novel sentiment 0.0017196120000000001
final sentiment 0.001704018
initial sentiment 0.001703654
data sets 0.0016967220000000001
data preparation 0.0016703380000000002
sentiment orientation 0.001632208
sentiment transfer 0.001626514
different words 0.001622836
sentiment orientations 0.001608979
word space 0.001606947
learning algorithm 0.001606552
sentiment orienta 0.001606189
new domain 0.0015897679999999999
space model 0.001516936
different documents 0.0014800360000000001
text classification 0.001361017
sentiment 0.00135461
classification problem 0.0013506170000000001
ent domain 0.0013446399999999998
vector space 0.00133905
different methods 0.0013202040000000002
complete algorithm 0.001293706
tion algorithm 0.001284715
ity matrix 0.0012827900000000002
text document 0.0012782940000000001
old domain 0.001276628
adjacency matrix 0.001275836
jacency matrix 0.001275836
algorithm prototype 0.0012735630000000001
transfer algorithm 0.0012514420000000002
different domains 0.001251412
proposed algorithm 0.001236832
algorithm initialization 0.00123208
algorithm introduction 0.00123208
algorithm calcu 0.00123208
document set 0.001179364
experimental results 0.001172603
same way 0.001154819
score vector 0.0011459999999999999
labeled training 0.001142091
other methods 0.0011278199999999999
experiment results 0.001100653
positive document 0.001091472
supervised learning 0.001089837
term vector 0.001080714
vector machine 0.001074723
novel approach 0.001073205
classification algo 0.001069459
training dataset 0.00106781
prototype classification 0.001066278
cosine similarity 0.001055786
timent classification 0.001050567
learning methods 0.0010462079999999999
support vector 0.001043845
frequency words 0.001034246
text pos 0.001024227
matrix 0.00102182
domain 0.00102054
document frequency 0.0010019500000000001
pivot features 0.001001563
content similarity 9.86011E-4
disappointing results 9.82418E-4
inverse document 9.81826E-4
algorithm 9.79538E-4
document sets 9.673220000000001E-4
model 9.52439E-4
correspondence learning 9.506499999999999E-4
whole document 9.50477E-4
documents score 9.50473E-4
score documents 9.50473E-4
unlabelled document 9.44713E-4
positive reviews 9.183050000000001E-4
test dataset 9.155599999999999E-4
chinese text 8.98738E-4
criminative learning 8.80176E-4
traditional text 8.75824E-4
ith text 8.54168E-4
new study 8.50325E-4
jth text 8.457429999999999E-4
electronics reviews 8.39305E-4
chinese reviews 8.263370000000001E-4
baseline methods 8.065209999999999E-4
future work 7.96557E-4
negative scores 7.93803E-4
relative importance 7.93201E-4
total number 7.92939E-4
positive scores 7.81689E-4
traditional classifier 7.796260000000001E-4
vector 7.74553E-4
classification 7.72253E-4
stock reviews 7.70094E-4
