deceptive opinion 0.0023223740000000003
deceptive reviews 0.002075764
deceptive features 0.002025234
opinion text 0.001999234
truthful reviews 0.001853005
other features 0.001839505
opinion spam 0.0018354250000000001
review data 0.001789021
deceptive approach 0.0017648020000000002
language features 0.001691971
mining opinion 0.0016893210000000001
available opinion 0.001683352
hotel reviews 0.001678822
truthful deceptive 0.0016536290000000002
deceptive truthful 0.0016536290000000002
ceptive opinion 0.001649433
deception features 0.001641558
disruptive opinion 0.001632364
deceptive opinions 0.001622682
positive reviews 0.001552196
review text 0.001543432
deceptive language 0.001543125
average feature 0.001487651
deceptive hotel 0.0014794460000000001
classifiers features 0.001468286
liwc features 0.0014297350000000001
consumer reviews 0.0014285720000000001
tive reviews 0.001422648
feature set 0.001416351
corresponding features 0.001413921
product review 0.001412843
truthful opinions 0.001399923
feature analysis 0.001393214
deceptive spam 0.0013894390000000001
fraudulent reviews 0.001384707
opinion 0.00138418
detection approach 0.001373829
ambiguous features 0.001354235
negative opinions 0.001353591
deceptive writing 0.001346039
positive review 0.001343004
features tradi 0.001335112
motivated features 0.001335112
deceptive accuracy 0.00130919
human deception 0.0012951
standard text 0.001283549
preceding feature 0.001280376
other work 0.0012763549999999999
identification approach 0.001270954
fake review 0.00126968
deceptive precision 0.001269451
feature weights 0.0012681609999999999
feature sets 0.00126286
travel review 0.001238788
review web 0.001231495
language model 0.0012299289999999998
deceptive opin 0.00122802
view data 0.001208519
human performance 0.001204367
deceptive state 0.00119104
example review 0.001188282
review website 0.00118672
review fraud 0.001175921
classification task 0.001173353
categorization approach 0.00116834
detection text 0.001162275
labeled data 0.001160881
reviews 0.00113757
psycholinguistic approach 0.0011370730000000002
data annotation 0.00112918
truthful writing 0.00112328
gathering data 0.0011088439999999999
model classifier 0.001108527
ment words 0.00110831
tion approach 0.001102067
initial approach 0.001101812
deception detection 0.001101739
text classifier 0.001098583
spatial information 0.001092026
features 0.00108704
combination approach 0.001080922
word count 0.001072852
detection work 0.001071111
evaluation approaches 0.001058528
human judges 0.001056174
ranking system 0.001041422
text analysis 0.001038294
other domains 0.001033538
other baselines 0.001029909
detection approaches 0.00102662
chicago hotel 0.001023914
human judge 0.001023288
detection performance 0.001011006
truthful opin 0.0010052610000000001
other kinds 0.0010035880000000001
other strategies 0.001003533
other submissions 0.001002864
human perfor 0.001002143
other computa 0.001001757
model parameters 0.00100045
