words polarity 0.00334167
different words 0.0032123300000000002
negative words 0.002884966
related words 0.0026166510000000002
similar words 0.002580147
words coverage 0.0025699520000000003
emotion lexicon 0.0025690599999999997
frequent words 0.002508623
annotated words 0.002491842
words order 0.0024544370000000003
words 0.00219561
negative sentiment 0.0020965660000000002
sentiment scores 0.002022269
emotion scores 0.001963659
such word 0.0018794789999999999
sentiment analysis 0.001860127
emotion analysis 0.0018015169999999999
single emotion 0.0017544989999999999
emotion label 0.001721022
emotion labels 0.0016956529999999999
sentiment lexica 0.0016891330000000002
precision emotion 0.001679748
affective lexicon 0.00166704
building sentiment 0.001659894
prior polarity 0.001640321
emotion lexica 0.001630523
emotion lexi 0.001616227
emotion lex 0.0016113389999999998
emotion recognition 0.0016108609999999999
votesµ emotion 0.001599332
lexicon coverage 0.001594802
word afraid 0.001575373
inquirer lexicon 0.001545936
word order 0.0015354869999999999
affect lexicon 0.001510513
only lexicon 0.001501652
lexicon creation 0.001474338
lexicon building 0.001473144
erage lexicon 0.001470138
posterior polarity 0.001430068
sentiment 0.00140721
emotion 0.0013486
different sizes 0.001270265
lexicon 0.00122046
other approaches 0.001181852
classification results 0.00116203
scores pos 0.001151062
classification task 0.0011470999999999999
polarity 0.00114606
social news 0.001136467
semantic similarity 0.001135273
positive connotation 0.001118034
other methods 0.001110715
other nlp 0.001080682
such results 0.001060339
other hand 0.001057739
other systems 0.0010565869999999999
affective text 0.001038807
classification tasks 0.001031613
other datasets 0.001022837
semantic associations 0.001021982
other sys 0.0010170869999999999
same dataset 0.0010124779999999998
news articles 0.001004487
same results 9.98494E-4
test set 9.89462E-4
prior polarities 9.797529999999999E-4
news headlines 9.77474E-4
media data 9.76932E-4
negative valence 9.751720000000001E-4
classification settings 9.73378E-4
negative customer 9.712830000000001E-4
such lists 9.65123E-4
pervised classification 9.64164E-4
many approaches 9.58852E-4
simple text 9.572179999999999E-4
related work 9.52793E-4
annotation approach 9.49375E-4
regression results 9.45965E-4
tive scores 9.42605E-4
news network 9.41468E-4
regression task 9.31035E-4
correlation scores 9.30155E-4
human precision 9.11586E-4
research purposes 9.103970000000001E-4
valence scores 9.00875E-4
original text 8.9695E-4
affective annotation 8.95818E-4
annotation task 8.91828E-4
numerical scores 8.70207E-4
ical scores 8.66281E-4
such cases 8.64255E-4
several annotators 8.57947E-4
text recognition 8.54488E-4
several reasons 8.51585E-4
general inquirer 8.49708E-4
text categoriza 8.44155E-4
language use 8.426379999999999E-4
emotions model 8.42052E-4
high coverage 8.32231E-4
