influenza tweets 0.002342843
negative tweets 0.002196995
twitter data 0.002008458
influenza tweet 0.002006223
few tweets 0.001952323
related tweets 0.0019436009999999999
other data 0.001839938
only tweets 0.001799892
fluenza tweets 0.001759399
tweets biases 0.001752192
tweet first 0.001750346
positive data 0.001742019
training data 0.0016665059999999999
sentiment analysis 0.001602138
test data 0.0015392449999999998
standard data 0.0015162029999999998
tweets 0.00151586
negative influenza 0.0015081180000000001
influenza detection 0.001466689
positive influenza 0.001462482
tweet person 0.001455422
patient data 0.001453708
data volume 0.001442444
experimental data 0.001417006
data points 0.001401722
huge data 0.001371227
clinical data 0.001367379
numerous data 0.001360768
such information 0.001356914
data groups 0.001352014
target influenza 0.001332191
feature window 0.0013321539999999999
twitter users 0.0013282
classification performance 0.001325821
web search 0.00129493
feature representation 0.001282254
negative classifier 0.0012683120000000002
web system 0.00123176
own influenza 0.001226876
twitter streams 0.001224737
influenza corpus 0.001197197
learning methods 0.001191303
twitter api 0.001191248
learning classifier 0.001184837
twitter texts 0.001179345
tweet 0.00117924
influenza surveillance 0.001175619
influenza patient 0.001174171
detection performance 0.001172224
detection method 0.001168917
similar twitter 0.0011655440000000001
search queries 0.0011542219999999999
influenza domain 0.0011498300000000001
training set 0.001144347
search engine 0.001143926
google search 0.001130756
same approach 0.0011284580000000002
influenza table 0.001127609
learning method 0.001126871
training dataset 0.001110865
influenza drugs 0.0011072669999999999
influenza reports 0.001099521
influenza negation 0.001096913
simple word 0.001093776
sentiment 0.00109209
real influenza 0.001092085
influenza epidemics 0.001086954
bad influenza 0.0010818429999999999
tive influenza 0.001081075
sentence classification 0.001074804
influenza surveil 0.001071458
negative annotation 0.001068519
other people 0.001068247
influenza severance 0.001068202
influenza warnings 0.0010633349999999999
influenza epi 0.0010633349999999999
actual influenza 0.001062375
influenza epidem 0.001061523
european influenza 0.001061523
avian influenza 0.001061523
influenza patients 0.001061523
useful information 0.00104717
system extracts 0.00104526
several research 0.001043607
social media 0.00103348
severance system 0.001033234
feature 0.00101634
previous methods 0.001005005
detailed results 9.99261E-4
general news 9.93696E-4
various methods 9.833860000000002E-4
side words 9.79653E-4
negative sentence 9.62636E-4
language processing 9.59449E-4
new approaches 9.45667E-4
news period 9.428380000000001E-4
current methods 9.37731E-4
epidemic detection 9.331509999999999E-4
excessive news 9.32189E-4
news periods 9.252920000000001E-4
