sentiment target 0.001801963
chinese word 0.0017297979999999998
model evaluation 0.001665911
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bayesian model 0.0016270500000000001
opinion target 0.0015798750000000001
markov model 0.001540052
target words 0.001514571
model ours 0.0015110969999999999
training data 0.001442403
sentiment score 0.001436162
sentiment analysis 0.001423663
political news 0.001418704
sentiment extraction 0.001415466
negative word 0.001396605
document target 0.0013868
opinion words 0.001361872
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level sentiment 0.001345371
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sentiment polarity 0.00132232
single news 0.001307596
word entities 0.001301168
word list 0.001287194
data set 0.001275863
positive sentiment 0.0012750679999999999
model 0.00126096
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extraction task 0.001253237
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sentence list 0.001241496
target list 0.001236755
subjectivity word 0.001235893
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current sentiment 0.001233278
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target clustering 0.001210427
clustering target 0.001210427
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opinion extraction 0.0011933780000000001
chinese people 0.001185866
sentiment cues 0.001182397
news article 0.001178799
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current sentence 0.00116863
sentence selection 0.001159453
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word segmenta 0.001151097
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chinese segmentation 0.001143887
news articles 0.001142463
chinese subjectivity 0.001132239
negative words 0.001128163
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chinese parser 0.0011173659999999998
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news streams 0.001109255
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ping algorithm 0.001103328
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extraction performance 0.0010966180000000002
pronoun target 0.001095558
time information 0.0010833610000000001
labeled data 0.001078389
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chinese gov 0.001044234
chinese subjec 0.001044234
chinese readers 0.001044234
chinese encyclopedia 0.001044234
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difficult task 0.001039879
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political analysis 0.001029282
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opinion targets 0.001016277
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regression models 0.001005099
cific task 0.00100391
topic number 0.001002999
other nlp 9.97272E-4
automatic opinion 9.93296E-4
limited data 9.88711E-4
positive words 9.876759999999998E-4
other parameters 9.82967E-4
labeling models 9.78712E-4
opinion expressions 9.71717E-4
analysis approach 9.63123E-4
separate words 9.5736E-4
