+ hR~Rt^RIt^RIt^RIt^RIHt^RIt^RIH t !RR4t !RR4t !RR 4t R#) a If you use the VADER sentiment analysis tools, please cite: Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. N)product)pairwisecJa]tRt^!toRtRtRtRtRt0RkRkRkRkRkR kR kR kR kR kRkRkRkRkRkRkRkRkRkRkRkRkRkRkRkRkRkRkR kR!kR"kR#kR$kR%kR&kR'kR(kR)kR*kR+kR,kR-kR.kR/kR0kR1kR2kR3kR4kR5kR6kR7kR8kR9kR:kR;kRkt /R?]bR@]bRA]bRB]bRC]bRD]bRE]bRF]bRG]bRH]bRI]bRJ]bRK]bRL]bRM]bRN]bRO]b/RP]bRQ]bRR]bRS]bRT]bRU]bRV]bRW]bRX]bRY]bRZ]bR[]bR\]bR]]bR^]bR_]bR`]bC/Ra]bRb]bRc]bRd]bRe]bRf]bRg]bRh]bRi]bRj]bRk]bRl]bRm]bRn]bRo]bRp]bRq]bCRr]Rs]Rt]Ru]Rv]Rw]Rx]Ry]Rz]R{]R|]R}]R~]R]R]/Ct R^R^RRRRR^RRRR/t ] P!R] P!]P 4 R24t.ROtRtRRltRRltRtRtVtR#)VaderConstantsz0 A class to keep the Vader lists and constants. gn?g~jt?aintarentcannotcantcouldntdarentdidntdoesntzain'tzaren'tzcan'tzcouldn'tzdaren'tzdidn'tzdoesn'tdonthadnthasnthaventisntmightntmustntneitherzdon'tzhadn'tzhasn'tzhaven'tzisn'tzmightn'tzmustn'tneedntzneedn'tnevernonenopenornotnothingnowhereoughtntshantshouldntuhuhwasntwerentzoughtn'tzshan'tz shouldn'tzuh-uhzwasn'tzweren'twithoutwontwouldntzwon'tzwouldn'trarelyseldomdespite absolutely amazinglyawfully completely considerably decidedlydeeplyeffing enormouslyentirely especially exceptionally extremely fabulouslyflippingflippinfrickingfrickinfriggingfrigginfullyfuckinggreatlyhellahighlyhugely incredibly intenselymajorlymoremost particularlypurelyquitereally remarkablyso substantially thoroughlytotally tremendouslyuber unbelievably unusuallyutterlyveryalmostbarelyhardlyz just enoughzkind ofkindakindofzkind-oflesslittle marginally occasionallypartlyscarcelyslightlysomewhatzsort ofsortasortofzsort-ofzthe shitzthe bombzbad ass?z yeah rightzcut the mustardz kiss of deathz hand to mouth[]cR#N)selfs&6/usr/lib/python3/dist-packages/nltk/sentiment/vader.py__init__VaderConstants.__init__s c aVPo\;QJdV3RlV4F 'gK RM RM!V3RlV44'dR#V'd=\;QJdRV4F 'gK RM RM !RV44'dR#\V4F4wr4VP4R8XgKVP4R8wgK3R# R#)z, Determine if input contains negation words c3H<"TFqP4S9xK R#5irklower).0word neg_wordss& rn )VaderConstants.negated..sA[Tzz|y([s"TFc3H"TFpRVP49xK R#5i)zn'tNrt)rvrws& rnryrzsA[T5DJJL([s "leastat)NEGATEanyrru)rm input_words include_ntfirstsecondrxs&&& @rnnegatedVaderConstants.negatedsKK 3A[A333A[A A A sA[AsssA[AAA%k2ME||~(U[[]d-B3rqc ZV\P!W,V,4, pV#)zd Normalize the score to be between -1 and 1 using an alpha that approximates the max expected value )mathsqrt)rmscorealpha norm_scores&&& rn normalizeVaderConstants.normalizes$ TYY '>?? rqc &RpVP4pWPP9doVPV,pV^8d VR,pVP4'd6V'd.V^8dW@P, pV#W@P,pV#)zP Check if the preceding words increase, decrease, or negate/nullify the valence )ru BOOSTER_DICTisupperC_INCR)rmrwvalence is_cap_diffscalar word_lowers&&&& rnscalar_inc_decVaderConstants.scalar_inc_decsz ZZ\ ** *&&z2F{" ||~~+Q;kk)F kk)F rqrlNgnҿgGzg).!?,;:-'"z!!z!!!z??z???z?!?z!?!z?!?!z!?!?)T))__name__ __module__ __qualname____firstlineno____doc__B_INCRB_DECRrN_SCALARr~rSPECIAL_CASE_IDIOMSrecompileescapestring punctuationREGEX_REMOVE_PUNCTUATION PUNC_LISTrorrr__static_attributes____classdictcell__ __classdict__s@rnrr!s< F FFH<<< <  <  <  < < < < < < < < < < !<" #<$ %<& '<( )<* +<, -<. /<0 1<2 3<4 5<6 7<8 9<: ;<< =<> ?<@ A<B C<D E<F G<H I<J K<L M<N O<P Q<R S<T U<V W<X Y<Z [<\ ]<^ _<` a<b c<d e<f g<h i<j k<l m<n o<p q<r s<t u<v w<FBCfCVC 6C f C  C V C &C &C fC FC fC C VC fC FC 6!C" F#C$ 6%C& F'C( 6)C* +C, 6-C. 6/C0 1C2 &3C4 &5C6 f7C8 V9C: 6;C< =C> ?C@ ACB &CCD ECF &GCH fICJ fKCL MCN fOCP 6QCR SCT UCV WCX VYCZ 6[C\ ]C^ &_C` &aCb &cCd veCf 6gCh &6&f&FFF6&6ECLN AA3b1 "zzAbii8J8J.K-LA*NOI(  rqrc<a]tRtRtoRtRtRtRtRtRt Vt R#) SentiTextizD Identify sentiment-relevant string-level properties of input text. c\V\4'g\VPR44pWnW nW0nVP 4VnVPVP4Vn R#)zutf-8N) isinstancestrencodetextrr_words_and_emoticonswords_and_emoticonsallcap_differentialr)rmr punc_listregex_remove_punctuations&&&&rnroSentiText.__init__ s^$$$t{{7+,D "(@%#'#<#<#>  33D4L4LMrqc VPPRVP4pVP4pVUu0uFp\ V4^8gKVkK pp\ VP V4Uu/uFpRPV4V^,bK pp\ W P 4Uu/uFpRPV4V^,bK ppTpVPV4V#uupiuupiuupi)zD Returns mapping of form: { 'cat,': 'cat', ',cat': 'cat', } ) rsubrsplitlenrrjoinupdate)rm no_punc_text words_onlywp punc_before punc_afterwords_punc_dicts& rn_words_plus_puncSentiText._words_plus_puncs4488TYYG !'') !+:As1vzaa :181TU1TArwwqz1Q4'1T U07 NN0ST0S1bggaj!A$&0S T%z* ;UTsC&C&2"C+-"C0c VPP4pVP4pVUu.uFp\V4^8gKVNK pp\ V4FwrCW29gK W#,W&K V#uupi)z Removes leading and trailing puncutation Leaves contractions and most emoticons Does not preserve punc-plus-letter emoticons (e.g. :D) )rrrr enumerate)rmwesrweis& rnrSentiText._words_and_emoticons+sn iioo//1/Cb3r7Q;rrC/s^EA$(,$ 0s A6A6c Rp^pVF$pVP4'gKV^, pK& \V4V, p^Tu;8d\V48dMV#RpV#)z Check whether just some words in the input are ALL CAPS :param list words: The words to inspect :returns: `True` if some but not all items in `words` are ALL CAPS FT)rr)rmwords is_different allcap_wordsrwcap_differentials&& rnrSentiText.allcap_differential9sc  D||~~! u: 4  ,#e* , Lrq)rrrrrN) rrrrrrorrrrrrs@rnrrs$ N( rqrcva]tRtRtoRtRRltRtRtRtRt Rt R t R t R t R tR tRtRtRtVtR#)SentimentIntensityAnalyzeriKz0 Give a sentiment intensity score to sentences. c\PPV4VnVP 4Vn\ 4VnR#rk)nltkdataload lexicon_file make_lex_dictlexiconr constants)rmrs&&rnro#SentimentIntensityAnalyzer.__init__Ps3!IINN<8))+ ')rqc /pVPPR4F8pVP4PR4R,wr4\V4W&K: V#)z& Convert lexicon file to a dictionary   :N)rrstripfloat)rmlex_dictlinerwmeasures& rnr(SentimentIntensityAnalyzer.make_lex_dictXsS%%++D1D"jjl006s;OT"7^HN2rqc <\WPPVPP4p.pVPpVFp^pVP V4pV\ V4^, 8d8VP4R8Xd#WG^,,P4R8Xg*VP4VPP9dVPV4KVPWbWWV4pK VPWC4pVPW14#)a Return a float for sentiment strength based on the input text. Positive values are positive valence, negative value are negative valence. :note: Hashtags are not taken into consideration (e.g. #BAD is neutral). If you are interested in processing the text in the hashtags too, then we recommend preprocessing your data to remove the #, after which the hashtag text may be matched as if it was a normal word in the sentence. kindof) rrrrrindexrrurappendsentiment_valence _but_check score_valence)rmr sentitext sentimentsritemrrs&& rnpolarity_scores*SentimentIntensityAnalyzer.polarity_scoresbs ..**DNN,S,S  ';;'DG#))$/AC+,q00JJLF*'A.446$>!2G||~~+Q;~~444G~~444G A;K+1,=>DDF<<( 55+1,=>A!|QH!|QG%kG"//gG!|"&"4"4WST"U+'<''aHG'"rqcVV^8dW#^, ,P4VP9dW#^, ,P4R8XdcW#^, ,P4R8wd?W#^, ,P4R8wdWPP,pV#V^8dkW#^, ,P4VP9d?W#^, ,P4R8XdWPP,pV#)r|r}rW)rurrr)rmrrrs&&&&rnr 'SentimentIntensityAnalyzer._least_checks E#E*002$,,F#E*002g=$E*002d:'A.446&@!NN$;$;; E#E*002$,,F#E*002g= 7 77Grqc<VUu.uFq3P4NK ppR0\V4,pV'd^VP\\ V444p\ V4F+wrgWe8dVR,W&&KWe8gK VR,W&&K- V#uupi)butg?rg)rusetrnextiterr)rmrrw_erbisidx sentiments&&& rnr%SentimentIntensityAnalyzer._but_checks6IJ6Isyy{6IJg/00 $**4S ?;B#,Z#89'03J$Y'03J$ $9 KsBcW#^, , RW#, 2pRPW#^, ,W#^, ,W#,4pW#^, , RW#^, , 2pRPW#^, ,W#^, ,W#^, ,4pRPW#^, ,W#^, ,4pWEWgV.p V F<p WPP9gKVPPV ,pM \V4^, V8dRW#, RW#^,, 2p WPP9dVPPV ,p\V4^, V^,8dkRPW#,W#^,,W#^,,4p WPP9dVPPV ,pWPP9gW`PP9dWPP ,pV#)r z{} {} {}z{} {})formatrrrrr) rmrrronezero twoonezerotwoone threetwoonethreetwo sequencesseqzeroone zeroonetwos &&&& rnr(SentimentIntensityAnalyzer._idioms_checks(Q/02E2H1IJ&& A & A &  " (A./q1DU1K0LM '' A & A & A & >> A &(;E(B &xH Cnn888..<r?totalrFrDrEsentiment_dicts&&& rnr(SentimentIntensityAnalyzer.score_valenceTs< :#j/*E#'#=#=e#J qy--~~//6H*.*E*Ej*Q 'Gi7++/499W--/dii009rQsD  ccLAAHnnrq