Russian
EXTRACTIVE
Russsian
ABSTRACTIVE
Considering the improvements over the majority baseline achieved by the RNN model for both non-English (on the average 22.76% relative improvement; 15.82% relative improvement on Spanish, 72.71% vs. 84.21%, 30.53% relative improvement on Turkish, 56.97% vs. 74.36%, 37.13% relative improvement on Dutch, 59.63% vs. 81.77%, and 7.55% relative improvement on Russian, 79.60% vs. 85.62%) and English test sets (27.34% relative improvement), we can draw the conclusion that our model is robust to handle multiple languages. Building separate models for each language requires both labeled and unlabeled data. Even though having lots of labeled data in every language is the perfect case, it is unrealistic. Therefore, eliminating the resource requirement in this resource-constrained task is crucial. The fact that machine translation can be used in reusing models from different languages is promising for reducing the data requirements.
FLOAT SELECTED: Table 3: Accuracy results (%) for RNN-based approach compared with majority baseline and lexicon-based baseline.
1806.04511
Introduction </s> With Ġthe Ġsteady Ġgrowth Ġin Ġthe Ġcommercial Ġwebsites Ġand Ġsocial Ġmedia Ġvenues , Ġthe Ġaccess Ġto Ġusers ' Ġreviews Ġhave Ġbecome Ġeasier . ĠAs Ġthe Ġamount Ġof Ġdata Ġthat Ġcan Ġbe Ġmined Ġfor Ġopinion Ġincreased , Ġcommercial Ġcompanies ' Ġinterests Ġfor Ġsentiment Ġanalysis Ġincreased Ġas Ġwell . ĠSent iment Ġanalysis Ġis Ġan Ġimportant Ġpart Ġof Ġunderstanding Ġuser Ġbehavior Ġand Ġopinions Ġon Ġproducts , Ġplaces , Ġor Ġservices . </s> Sent iment Ġanalysis Ġhas Ġlong Ġbeen Ġstudied Ġby Ġthe Ġresearch Ġcommunity , Ġleading Ġto Ġseveral Ġsentiment - related Ġresources Ġsuch Ġas Ġsentiment Ġdiction aries Ġthat Ġcan Ġbe Ġused Ġas Ġfeatures Ġfor Ġmachine Ġlearning Ġmodels ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ. ĠThese Ġresources Ġhelp Ġincrease Ġsentiment Ġanalysis Ġaccur acies ; Ġhowever , Ġthey Ġare Ġhighly Ġdependent Ġon Ġlanguage Ġand Ġrequire Ġresearchers Ġto Ġbuild Ġsuch Ġresources Ġfor Ġevery Ġlanguage Ġto Ġprocess . </s> Feature Ġengineering Ġis Ġa Ġlarge Ġpart Ġof Ġthe Ġmodel Ġbuilding Ġphase Ġfor Ġmost Ġsentiment Ġanalysis Ġand Ġemotion Ġdetection Ġmodels ĠB IB REF 4 Ġ. ĠD etermin ing Ġthe Ġcorrect Ġset Ġof Ġfeatures Ġis Ġa Ġtask Ġthat Ġrequires Ġthorough Ġinvestigation . ĠFurthermore , Ġthese Ġfeatures Ġare Ġmostly Ġlanguage Ġand Ġdataset Ġdependent Ġmaking Ġit Ġeven Ġfurther Ġchallenging Ġto Ġbuild Ġmodels Ġfor Ġdifferent Ġlanguages . ĠFor Ġexample , Ġthe Ġsentiment Ġand Ġemotion Ġlex icons , Ġas Ġwell Ġas Ġpre - trained Ġword Ġembed d ings Ġare Ġnot Ġcompletely Ġtransfer able Ġto Ġother Ġlanguages Ġwhich Ġrepl icates Ġthe Ġefforts Ġfor Ġevery Ġlanguage Ġthat Ġusers Ġwould Ġlike Ġto Ġbuild Ġsentiment Ġclassification Ġmodels Ġon . ĠFor Ġlanguages Ġand Ġtasks Ġwhere Ġthe Ġdata Ġis Ġlimited , Ġextracting Ġthese Ġfeatures , Ġbuilding Ġlanguage Ġmodels , Ġtraining Ġword Ġembed d ings , Ġand Ġcreating Ġlex icons Ġare Ġbig Ġchallenges . ĠIn Ġaddition Ġto Ġthe Ġfeature Ġengineering Ġeffort , Ġthe Ġmachine Ġlearning Ġmodels ' Ġparameters Ġalso Ġneed Ġto Ġbe Ġtuned Ġseparately Ġfor Ġeach Ġlanguage Ġto Ġget Ġthe Ġoptimal Ġresults . </s> In Ġthis Ġpaper , Ġwe Ġtake Ġa Ġdifferent Ġapproach . ĠWe Ġbuild Ġa Ġreusable Ġsentiment Ġanalysis Ġmodel Ġthat Ġdoes Ġnot Ġutilize Ġany Ġlex icons . ĠOur Ġgoal Ġis Ġto Ġevaluate Ġhow Ġwell Ġa Ġgeneric Ġmodel Ġcan Ġbe Ġused Ġto Ġmine Ġopinion Ġin Ġdifferent Ġlanguages Ġwhere Ġdata Ġis Ġmore Ġlimited Ġthan Ġthe Ġlanguage Ġwhere Ġthe Ġgeneric Ġmodel Ġis Ġtrained Ġon . ĠTo Ġthat Ġend , Ġwe Ġbuild Ġa Ġtraining Ġset Ġthat Ġcontains Ġreviews Ġfrom Ġdifferent Ġdomains Ġin ĠEnglish Ġ( e . g ., Ġmovie Ġreviews , Ġproduct Ġreviews ) Ġand Ġtrain Ġa Ġrecurrent Ġneural Ġnetwork Ġ( R NN ) Ġmodel Ġto Ġpredict Ġpol arity Ġof Ġthose Ġreviews . ĠThen Ġfocusing Ġon Ġa Ġdomain , Ġwe Ġmake Ġthe Ġmodel Ġspecialized Ġin Ġthat Ġdomain Ġby Ġusing Ġthe Ġtrained Ġweights Ġfrom Ġthe Ġlarger Ġdata Ġand Ġfurther Ġtraining Ġwith Ġdata Ġon Ġa Ġspecific Ġdomain . ĠTo Ġevaluate Ġthe Ġre us ability Ġof Ġthe Ġsentiment Ġanalysis Ġmodel , Ġwe Ġtest Ġwith Ġnon - English Ġdatasets . ĠWe Ġfirst Ġtranslate Ġthe Ġtest Ġset Ġto ĠEnglish Ġand Ġuse Ġthe Ġpre - trained Ġmodel Ġto Ġscore Ġpol arity Ġin Ġthe Ġtranslated Ġtext . ĠIn Ġthis Ġway , Ġour Ġproposed Ġapproach Ġeliminates Ġthe Ġneed Ġto Ġtrain Ġlanguage - dependent Ġmodels , Ġuse Ġof Ġsentiment Ġlex icons Ġand Ġword Ġembed d ings Ġfor Ġeach Ġlanguage . ĠOur Ġexperiments Ġshow Ġthat Ġa Ġgeneral izable Ġsentiment Ġanalysis Ġmodel Ġcan Ġbe Ġutilized Ġsuccessfully Ġto Ġperform Ġopinion Ġmining Ġfor Ġlanguages Ġthat Ġdo Ġnot Ġhave Ġenough Ġresources Ġto Ġtrain Ġspecific Ġmodels . </s> The Ġcontributions Ġof Ġthis Ġstudy Ġare ; Ġ1 ) Ġa Ġrobust Ġapproach Ġthat Ġutilizes Ġmachine Ġtranslation Ġto Ġreuse Ġa Ġmodel Ġtrained Ġon Ġone Ġlanguage Ġin Ġother Ġlanguages , Ġ2 ) Ġan ĠR NN - based Ġapproach Ġto Ġeliminate Ġfeature Ġextraction Ġas Ġwell Ġas Ġresource Ġrequirements Ġfor Ġsentiment Ġanalysis , Ġand Ġ3 ) Ġa Ġtechnique Ġthat Ġstatistically Ġsignificantly Ġoutper forms Ġbas elines Ġfor Ġmult ilingual Ġsentiment Ġanalysis Ġtask Ġwhen Ġdata Ġis Ġlimited . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthis Ġstudy Ġis Ġthe Ġfirst Ġto Ġapply Ġa Ġdeep Ġlearning Ġmodel Ġto Ġthe Ġmult ilingual Ġsentiment Ġanalysis Ġtask . </s> Related ĠWork </s> There Ġis Ġa Ġrich Ġbody Ġof Ġwork Ġin Ġsentiment Ġanalysis Ġincluding Ġsocial Ġmedia Ġplatforms Ġsuch Ġas ĠTwitter ĠB IB REF 5 Ġand ĠFacebook ĠB IB REF 4 Ġ. ĠOne Ġcommon Ġfactor Ġin Ġmost Ġof Ġthe Ġsentiment Ġanalysis Ġwork Ġis Ġthat Ġfeatures Ġthat Ġare Ġspecific Ġto Ġsentiment Ġanalysis Ġare Ġextracted Ġ( e . g ., Ġsentiment Ġlex icons ) Ġand Ġused Ġin Ġdifferent Ġmachine Ġlearning Ġmodels . ĠLex ical Ġresources ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 4 Ġfor Ġsentiment Ġanalysis Ġsuch Ġas ĠSent i Word Net ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, Ġlinguistic Ġfeatures Ġand Ġexpressions ĠB IB REF 8 Ġ, Ġpol arity Ġdiction aries ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ, Ġother Ġfeatures Ġsuch Ġas Ġtopic - oriented Ġfeatures Ġand Ġsyntax ĠB IB REF 9 Ġ, Ġemotion Ġtokens ĠB IB REF 10 Ġ, Ġword Ġvectors ĠB IB REF 11 Ġ, Ġand Ġem ographics ĠB IB REF 12 Ġare Ġsome Ġof Ġthe Ġinformation Ġthat Ġare Ġfound Ġuseful Ġfor Ġimproving Ġsentiment Ġanalysis Ġaccur acies . ĠAlthough Ġthese Ġfeatures Ġare Ġbeneficial , Ġextracting Ġthem Ġrequires Ġlanguage - dependent Ġdata Ġ( e . g ., Ġa Ġsentiment Ġdictionary Ġfor ĠSpanish Ġis Ġtrained Ġon ĠSpanish Ġdata Ġinstead Ġof Ġusing Ġall Ġdata Ġfrom Ġdifferent Ġlanguages ). </s> Our Ġgoal Ġin Ġthis Ġwork Ġis Ġto Ġstream line Ġthe Ġfeature Ġengineering Ġphase Ġby Ġnot Ġrelying Ġon Ġany Ġdictionary Ġother Ġthan ĠEnglish Ġword Ġembed d ings Ġthat Ġare Ġtrained Ġon Ġany Ġdata Ġ( i . e . Ġnot Ġnecessarily Ġsentiment Ġanalysis Ġcorpus ). ĠTo Ġthat Ġend , Ġwe Ġutilize Ġoff - the - she lf Ġmachine Ġtranslation Ġtools Ġto Ġfirst Ġtranslate Ġcorpor a Ġto Ġthe Ġlanguage Ġwhere Ġmore Ġtraining Ġdata Ġis Ġavailable Ġand Ġuse Ġthe Ġtranslated Ġcorpor a Ġto Ġdo Ġinference Ġon . </s> Machine Ġtranslation Ġfor Ġmult ilingual Ġsentiment Ġanalysis Ġhas Ġalso Ġseen Ġattention Ġfrom Ġresearchers . ĠHirosh i Ġet Ġal . ĠB IB REF 13 Ġtranslated Ġonly Ġsentiment Ġunits Ġwith Ġa Ġpattern - based Ġapproach . ĠBal ah ur Ġand ĠT urch i ĠB IB REF 14 Ġused Ġun i - gram s , Ġbi - gram s Ġand Ġtf - id f Ġfeatures Ġfor Ġbuilding Ġsupport Ġvector Ġmachines Ġon Ġtranslated Ġtext . ĠBoyd - Gr aber Ġand ĠRes nik ĠB IB REF 15 Ġbuilt ĠLat ent ĠDir ich let ĠAll ocation Ġmodels Ġto Ġinvestigate Ġhow Ġmult ilingual Ġconcepts Ġare Ġclustered Ġinto Ġtopics . ĠMohammed Ġet Ġal . ĠB IB REF 16 Ġtranslate ĠTwitter Ġposts Ġto ĠEnglish Ġas Ġwell Ġas Ġthe ĠEnglish Ġsentiment Ġlex icons . ĠTel lez Ġet Ġal . ĠB IB REF 17 Ġpropose Ġa Ġframework Ġwhere Ġlanguage - dependent Ġand Ġindependent Ġfeatures Ġare Ġused Ġwith Ġan ĠS VM Ġclass ifier . ĠThese Ġmachine Ġlearning Ġapproaches Ġalso Ġrequire Ġa Ġfeature Ġextraction Ġphase Ġwhere Ġwe Ġeliminate Ġby Ġincorporating Ġa Ġdeep Ġlearning Ġapproach Ġthat Ġdoes Ġthe Ġfeature Ġlearning Ġintrinsically . ĠFurther , ĠWan ĠB IB REF 18 Ġuses Ġan Ġensemble Ġapproach Ġwhere Ġthe Ġresources Ġ( e . g ., Ġlex icons ) Ġin Ġboth Ġthe Ġoriginal Ġlanguage Ġand Ġthe Ġtranslated Ġlanguage Ġare Ġused ĠâĢĵ Ġrequiring Ġresources Ġto Ġbe Ġpresent Ġin Ġboth Ġlanguages . ĠBrooke Ġet Ġal . ĠB IB REF 19 Ġalso Ġuse Ġmultiple Ġdiction aries . </s> In Ġthis Ġpaper , Ġwe Ġaddress Ġthe Ġresource Ġbottleneck Ġof Ġthese Ġtranslation - based Ġapproaches Ġand Ġpropose Ġa Ġdeep Ġlearning Ġapproach Ġthat Ġdoes Ġnot Ġrequire Ġany Ġdiction aries . </s> Method ology </s> In Ġorder Ġto Ġeliminate Ġthe Ġneed Ġto Ġfind Ġdata Ġand Ġbuild Ġseparate Ġmodels Ġfor Ġeach Ġlanguage , Ġwe Ġpropose Ġa Ġmult ilingual Ġapproach Ġwhere Ġa Ġsingle Ġmodel Ġis Ġbuilt Ġin Ġthe Ġlanguage Ġwhere Ġthe Ġlargest Ġresources Ġare Ġavailable . ĠIn Ġthis Ġpaper Ġwe Ġfocus Ġon ĠEnglish Ġas Ġthere Ġare Ġseveral Ġsentiment Ġanalysis Ġdatasets Ġin ĠEnglish . ĠTo Ġmake Ġthe ĠEnglish Ġsentiment Ġanalysis Ġmodel Ġas Ġgeneral izable Ġas Ġpossible , Ġwe Ġfirst Ġstart Ġby Ġtraining Ġwith Ġa Ġlarge Ġdataset Ġthat Ġhas Ġproduct Ġreviews Ġfor Ġdifferent Ġcategories . ĠThen , Ġusing Ġthe Ġtrained Ġweights Ġfrom Ġthe Ġlarger Ġgeneric Ġdataset , Ġwe Ġmake Ġthe Ġmodel Ġmore Ġspecialized Ġfor Ġa Ġspecific Ġdomain . ĠWe Ġfurther Ġtrain Ġthe Ġmodel Ġwith Ġdomain - specific ĠEnglish Ġreviews Ġand Ġuse Ġthis Ġtrained Ġmodel Ġto Ġscore Ġreviews Ġthat Ġshare Ġthe Ġsame Ġdomain Ġfrom Ġdifferent Ġlanguages . ĠTo Ġbe Ġable Ġto Ġemploy Ġthe Ġtrained Ġmodel , Ġtest Ġsets Ġare Ġfirst Ġtranslated Ġto ĠEnglish Ġvia Ġmachine Ġtranslation Ġand Ġthen Ġinference Ġtakes Ġplace . ĠFigure ĠFIG REF 1 Ġshows Ġour Ġmult ilingual Ġsentiment Ġanalysis Ġapproach . ĠIt Ġis Ġimportant Ġto Ġnote Ġthat Ġthis Ġapproach Ġdoes Ġnot Ġutilize Ġany Ġresource Ġin Ġany Ġof Ġthe Ġlanguages Ġof Ġthe Ġtest Ġsets Ġ( e . g ., Ġword Ġembed d ings , Ġlex icons , Ġtraining Ġset ). </s> Deep Ġlearning Ġapproaches Ġhave Ġbeen Ġsuccessful Ġin Ġmany Ġapplications Ġranging Ġfrom Ġcomputer Ġvision Ġto Ġnatural Ġlanguage Ġprocessing ĠB IB REF 20 Ġ. ĠRec urrent Ġneural Ġnetwork Ġ( R NN ) Ġincluding ĠLong ĠShort ĠTerm ĠMemory Ġ( L ST M ) Ġand ĠG ated ĠRec urrent ĠUnits Ġ( GR U ) Ġare Ġsubs ets Ġof Ġdeep Ġlearning Ġalgorithms Ġwhere Ġthe Ġdependencies Ġbetween Ġtokens Ġcan Ġbe Ġused Ġby Ġthe Ġmodel . ĠThese Ġmodels Ġcan Ġalso Ġbe Ġused Ġwith Ġvariable Ġlength Ġinput Ġvectors Ġwhich Ġmakes Ġthem Ġsuitable Ġfor Ġtext Ġinput . ĠL ST M Ġand ĠGR U Ġmodels Ġallow Ġoperations Ġof Ġsequences Ġof Ġvectors Ġover Ġtime Ġand Ġhave Ġthe Ġcapability Ġto Ġ` remember ' Ġprevious Ġinformation ĠB IB REF 20 Ġ. ĠR NN Ġhave Ġbeen Ġfound Ġuseful Ġfor Ġseveral Ġnatural Ġlanguage Ġprocessing Ġtasks Ġincluding Ġlanguage Ġmodeling , Ġtext Ġclassification , Ġmachine Ġtranslation . ĠR NN Ġcan Ġalso Ġutilize Ġpre - trained Ġword Ġembed d ings Ġ( n umeric Ġvector Ġrepresentations Ġof Ġwords Ġtrained Ġon Ġunl abel ed Ġdata ) Ġwithout Ġrequiring Ġhand - crafted Ġfeatures . ĠTherefore Ġin Ġthis Ġpaper , Ġwe Ġemploy Ġan ĠR NN Ġarchitecture Ġthat Ġtakes Ġtext Ġand Ġpre - trained Ġword Ġembed d ings Ġas Ġinputs Ġand Ġgenerates Ġa Ġclassification Ġresult . ĠWord Ġembed d ings Ġrepresent Ġwords Ġas Ġnumeric Ġvectors Ġand Ġcapture Ġsemantic Ġinformation . ĠThey Ġare Ġtrained Ġin Ġan Ġun super vised Ġfashion Ġmaking Ġit Ġuseful Ġfor Ġour Ġtask . </s> The Ġsentiment Ġanalysis Ġmodel Ġthat Ġis Ġtrained Ġon ĠEnglish Ġreviews Ġhas Ġtwo Ġbid irection al Ġlayers , Ġeach Ġwith Ġ40 Ġneurons Ġand Ġa Ġdrop out ĠB IB REF 21 Ġof Ġ0 . 2 Ġis Ġused . ĠThe Ġtraining Ġphase Ġtakes Ġpre - trained Ġword Ġembed d ings Ġand Ġreviews Ġin Ġtextual Ġformat , Ġthen Ġpredicts Ġthe Ġpol arity Ġof Ġthe Ġreviews . ĠFor Ġthis Ġstudy , Ġan Ġembed ding Ġlength Ġof Ġ100 Ġis Ġused Ġ( i . e ., Ġeach Ġword Ġis Ġrepresented Ġby Ġa Ġvector Ġof Ġlength Ġ100 ). ĠWe Ġutilized Ġpre - trained Ġglobal Ġvectors ĠB IB REF 22 Ġ. ĠThe Ġtraining Ġphase Ġis Ġdepicted Ġin ĠFigure ĠFIG REF 2 Ġ. </s> Exper iments </s> To Ġevaluate Ġthe Ġproposed Ġapproach Ġfor Ġmult ilingual Ġsentiment Ġanalysis Ġtask , Ġwe Ġconducted Ġexperiments . ĠThis Ġsection Ġfirst Ġpresents Ġthe Ġcorpor a Ġused Ġin Ġthis Ġstudy Ġfollowed Ġby Ġexperimental Ġresults . </s> Throughout Ġour Ġexperiments , Ġwe Ġuse ĠSAS ĠDeep ĠLearning ĠTool kit . ĠFor Ġmachine Ġtranslation , ĠGoogle Ġtranslation ĠAPI Ġis Ġused . </s> Cor pora </s> Two Ġsets Ġof Ġcorpor a Ġare Ġused Ġin Ġthis Ġstudy , Ġboth Ġare Ġpublicly Ġavailable . ĠThe Ġfirst Ġset Ġconsists Ġof ĠEnglish Ġreviews Ġand Ġthe Ġsecond Ġset Ġcontains Ġrestaurant Ġreviews Ġfrom Ġfour Ġdifferent Ġlanguages Ġ( Spanish , ĠTurkish , ĠDutch , ĠRussian ). ĠWe Ġfocus Ġon Ġpol arity Ġdetection Ġin Ġreviews , Ġtherefore Ġall Ġdatasets Ġin Ġthis Ġstudy Ġhave Ġtwo Ġclass Ġvalues Ġ( positive , Ġnegative ). </s> With Ġthe Ġgoal Ġof Ġbuilding Ġa Ġgeneral izable Ġsentiment Ġanalysis Ġmodel , Ġwe Ġused Ġthree Ġdifferent Ġtraining Ġsets Ġas Ġprovided Ġin ĠTable ĠT AB REF 5 Ġ. ĠOne Ġof Ġthese Ġthree Ġdatasets Ġ( Amazon Ġreviews ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ) Ġis Ġlarger Ġand Ġhas Ġproduct Ġreviews Ġfrom Ġseveral Ġdifferent Ġcategories Ġincluding Ġbook Ġreviews , Ġelectronics Ġproducts Ġreviews , Ġand Ġapplication Ġreviews . ĠThe Ġother Ġtwo Ġdatasets Ġare Ġto Ġmake Ġthe Ġmodel Ġmore Ġspecialized Ġin Ġthe Ġdomain . ĠIn Ġthis Ġpaper Ġwe Ġfocus Ġon Ġrestaurant Ġreviews Ġas Ġour Ġdomain Ġand Ġuse ĠYelp Ġrestaurant Ġreviews Ġdataset Ġextracted Ġfrom ĠYelp ĠDat as et ĠChallenge ĠB IB REF 25 Ġand Ġrestaurant Ġreviews Ġdataset Ġas Ġpart Ġof Ġa ĠK agg le Ġcompetition ĠB IB REF 26 Ġ. </s> For Ġevaluation Ġof Ġthe Ġmult ilingual Ġapproach , Ġwe Ġuse Ġfour Ġlanguages . ĠThese Ġdatasets Ġare Ġpart Ġof ĠSem E val - 2016 ĠChallenge ĠTask Ġ5 ĠB IB REF 27 Ġ, ĠB IB REF 28 Ġ. ĠTable ĠT AB REF 7 Ġshows Ġthe Ġnumber Ġof Ġobservations Ġin Ġeach Ġtest Ġcorpus . </s> Exper imental ĠResults </s> For Ġexperimental Ġresults , Ġwe Ġreport Ġmajority Ġbaseline Ġfor Ġeach Ġlanguage Ġwhere Ġthe Ġmajority Ġbaseline Ġcorresponds Ġto Ġa Ġmodel 's Ġaccuracy Ġif Ġit Ġalways Ġpredicts Ġthe Ġmajority Ġclass Ġin Ġthe Ġdataset . ĠFor Ġexample , Ġif Ġthe Ġdataset Ġhas Ġ60 % Ġof Ġall Ġreviews Ġpositive Ġand Ġ40 % Ġnegative , Ġmajority Ġbaseline Ġwould Ġbe Ġ60 % Ġbecause Ġa Ġmodel Ġthat Ġalways Ġpredicts ĠâĢ ľ positive âĢ Ŀ Ġwill Ġbe Ġ60 % Ġaccurate Ġand Ġwill Ġmake Ġmistakes Ġ40 % Ġof Ġthe Ġtime . </s> In Ġaddition Ġto Ġthe Ġmajority Ġbaseline , Ġwe Ġalso Ġcompare Ġour Ġresults Ġwith Ġa Ġlex icon - based Ġapproach . ĠWe Ġuse ĠSent i Word Net ĠB IB REF 29 Ġto Ġobtain Ġa Ġpositive Ġand Ġa Ġnegative Ġsentiment Ġscore Ġfor Ġeach Ġtoken Ġin Ġa Ġreview . ĠThen Ġsum Ġof Ġpositive Ġsentiment Ġscores Ġand Ġnegative Ġsentiment Ġscores Ġfor Ġeach Ġreview Ġis Ġobtained Ġby Ġsum ming Ġup Ġthe Ġscores Ġfor Ġeach Ġtoken . ĠIf Ġthe Ġpositive Ġsum Ġscore Ġfor Ġa Ġgiven Ġreview Ġis Ġgreater Ġthan Ġthe Ġnegative Ġsum Ġscore , Ġwe Ġaccept Ġthat Ġreview Ġas Ġa Ġpositive Ġreview . ĠIf Ġnegative Ġsum Ġis Ġlarger Ġthan Ġor Ġequal Ġto Ġthe Ġpositive Ġsum , Ġthe Ġreview Ġis Ġlabeled Ġas Ġa Ġnegative Ġreview . </s> R NN Ġoutper forms Ġboth Ġbas elines Ġin Ġall Ġfour Ġdatasets Ġ( see ĠTable ĠT AB REF 9 Ġ). ĠAlso Ġfor ĠSpanish Ġrestaurant Ġreview , Ġthe Ġlex icon - based Ġbaseline Ġis Ġbelow Ġthe Ġmajority Ġbaseline Ġwhich Ġshows Ġthat Ġsolely Ġtranslating Ġdata Ġand Ġusing Ġlex icons Ġis Ġnot Ġsufficient Ġto Ġachieve Ġgood Ġresults Ġin Ġmult ilingual Ġsentiment Ġanalysis . </s> Among Ġthe Ġwrong Ġclass ifications Ġfor Ġeach Ġtest Ġset , Ġwe Ġcalculated Ġthe Ġpercentage Ġof Ġfalse Ġpositives Ġand Ġfalse Ġnegatives . ĠTable ĠT AB REF 10 Ġshows Ġthe Ġdistribution Ġof Ġfalse Ġpositives Ġand Ġfalse Ġnegatives Ġfor Ġeach Ġclass . ĠIn Ġall Ġfour Ġclasses , Ġthe Ġnumber Ġof Ġfalse Ġnegatives Ġare Ġmore Ġthan Ġthe Ġnumber Ġof Ġfalse Ġpositives . ĠThis Ġcan Ġbe Ġexplained Ġby Ġthe Ġun balanced Ġtraining Ġdataset Ġwhere Ġthe Ġnumber Ġof Ġpositive Ġreviews Ġare Ġmore Ġthan Ġthe Ġnumber Ġof Ġnegative Ġreviews Ġ( 59 , 577 Ġvs Ġ17 , 132 ). </s> To Ġbe Ġable Ġto Ġsee Ġthe Ġdifference Ġbetween Ġbaseline Ġand ĠR NN , Ġwe Ġtook Ġeach Ġmethod 's Ġresults Ġas Ġa Ġgroup Ġ( 4 Ġvalues : Ġone Ġfor Ġeach Ġlanguage ) Ġand Ġcompared Ġthe Ġmeans . ĠPost Ġhoc Ġcomparisons Ġusing Ġthe ĠTu key ĠH SD Ġtest Ġindicated Ġthat Ġthe Ġmean Ġaccur acies Ġfor Ġbas elines Ġ( majority Ġand Ġlex icon - based ) Ġare Ġsignificantly Ġdifferent Ġthan ĠR NN Ġaccur acies Ġas Ġcan Ġbe Ġseen Ġin ĠTable ĠT AB REF 12 Ġ( family - wise Ġerror Ġrate = 0 . 06 ). ĠWhen ĠR NN Ġis Ġcompared Ġwith Ġlex icon - based Ġbaseline Ġand Ġmajority Ġbaseline , Ġthe Ġnull Ġhypothesis Ġcan Ġbe Ġrejected Ġmeaning Ġthat Ġeach Ġtest Ġis Ġsignificant . ĠIn Ġaddition Ġto Ġthese Ġcomparisons , Ġwe Ġalso Ġcalculated Ġthe Ġeffect Ġsizes Ġ( using ĠCohen 's Ġd ) Ġbetween Ġthe Ġbas elines Ġand Ġour Ġmethod . ĠThe Ġresults Ġare Ġalign ing Ġwith ĠTu key ĠH SD Ġresults Ġsuch Ġthat Ġwhile Ġour Ġmethod Ġversus Ġbas elines Ġhave Ġvery Ġlarge Ġeffect Ġsizes , Ġlex icon - based Ġbaseline Ġand Ġmajority Ġbaseline Ġhave Ġnegligible Ġeffect Ġsize . </s> Figure ĠFIG REF 11 Ġshows Ġthe Ġdifferences Ġin Ġminimum Ġand Ġmaximum Ġvalues Ġof Ġall Ġthree Ġapproaches . ĠAs Ġthe Ġfigure Ġshows , ĠR NN Ġsignificantly Ġoutper forms Ġboth Ġbas elines Ġfor Ġthe Ġsentiment Ġclassification Ġtask . </s> Discussion </s> One Ġof Ġthe Ġcrucial Ġelements Ġwhile Ġusing Ġmachine Ġtranslation Ġis Ġto Ġhave Ġhighly Ġaccurate Ġtranslations . ĠIt Ġis Ġlikely Ġthat Ġnon - English Ġwords Ġwould Ġnot Ġhave Ġword Ġembed d ings , Ġwhich Ġwill Ġdramatically Ġaffect Ġthe Ġeffectiveness Ġof Ġthe Ġsystem . ĠWe Ġanalyzed Ġthe Ġeffect Ġof Ġincorrect Ġtranslations Ġinto Ġour Ġapproach . ĠTo Ġthat Ġend , Ġwe Ġextracted Ġall Ġwrong Ġpredictions Ġfrom Ġthe Ġtest Ġset Ġand Ġcomputed Ġthe Ġratio Ġof Ġmis class ifications Ġthat Ġhave Ġnon - English Ġwords Ġin Ġthem . ĠWe Ġfirst Ġextracted Ġall Ġmis class ifications Ġfor Ġa Ġgiven Ġlanguage Ġand Ġfor Ġeach Ġobservation Ġin Ġthe Ġmis class ification Ġset , Ġwe Ġiter ated Ġthrough Ġeach Ġtoken Ġto Ġcheck Ġif Ġthe Ġtoken Ġis Ġin ĠEnglish . ĠIn Ġthis Ġway , Ġwe Ġcounted Ġthe Ġnumber Ġof Ġobservations Ġthat Ġcontained Ġat Ġleast Ġone Ġnon - English Ġword Ġand Ġdivided Ġthat Ġwith Ġthe Ġsize Ġof Ġthe Ġmis class ifications Ġset . ĠWe Ġused Ġthis Ġratio Ġto Ġinvestigate Ġthe Ġeffect Ġof Ġmachine Ġtranslation Ġerrors . </s> We Ġfound Ġthat Ġ25 . 84 % Ġof ĠDutch , Ġ21 . 76 % Ġof ĠTurkish , Ġ24 . 46 % ĠSpanish , Ġand Ġ10 . 71 % Ġof ĠRussian Ġreviews Ġthat Ġwere Ġmis classified Ġhad Ġnon - English Ġwords Ġin Ġthem . ĠThese Ġnon - English Ġwords Ġmight Ġbe Ġcausing Ġthe Ġmis class ifications . ĠHowever , Ġa Ġlarge Ġportion Ġof Ġthe Ġmiss class ifications Ġis Ġnot Ġcaused Ġdue Ġto Ġnot - trans lated Ġwords . ĠAt Ġthe Ġend , Ġthe Ġmachine Ġtranslation Ġerrors Ġhas Ġsome Ġbut Ġnot Ġnoticeable Ġeffects Ġon Ġour Ġmodel . ĠTherefore , Ġwe Ġcan Ġclaim Ġthat Ġmachine Ġtranslation Ġpreserves Ġmost Ġof Ġthe Ġinformation Ġnecessary Ġfor Ġsentiment Ġanalysis . </s> We Ġalso Ġevaluated Ġour Ġmodel Ġwith Ġan ĠEnglish Ġcorpus ĠB IB REF 27 Ġto Ġsee Ġits Ġperformance Ġwithout Ġany Ġinterference Ġfrom Ġmachine Ġtranslation Ġerrors . ĠUsing Ġthe ĠEnglish Ġdata Ġfor Ġtesting , Ġthe Ġmodel Ġachieved Ġ87 . 06 % Ġaccuracy Ġwhere Ġa Ġmajority Ġbaseline Ġwas Ġ68 . 37 % Ġand Ġthe Ġlex icon - based Ġbaseline Ġwas Ġ60 . 10 %. </s> Considering Ġthe Ġimprovements Ġover Ġthe Ġmajority Ġbaseline Ġachieved Ġby Ġthe ĠR NN Ġmodel Ġfor Ġboth Ġnon - English Ġ( on Ġthe Ġaverage Ġ22 . 76 % Ġrelative Ġimprovement ; Ġ15 . 82 % Ġrelative Ġimprovement Ġon ĠSpanish , Ġ72 . 71 % Ġvs . Ġ84 . 21 %, Ġ30 . 53 % Ġrelative Ġimprovement Ġon ĠTurkish , Ġ56 . 97 % Ġvs . Ġ74 . 36 %, Ġ37 . 13 % Ġrelative Ġimprovement Ġon ĠDutch , Ġ59 . 63 % Ġvs . Ġ81 . 77 %, Ġand Ġ7 . 55 % Ġrelative Ġimprovement Ġon ĠRussian , Ġ79 . 60 % Ġvs . Ġ85 . 62 %) Ġand ĠEnglish Ġtest Ġsets Ġ( 27 . 34 % Ġrelative Ġimprovement ), Ġwe Ġcan Ġdraw Ġthe Ġconclusion Ġthat Ġour Ġmodel Ġis Ġrobust Ġto Ġhandle Ġmultiple Ġlanguages . ĠBuilding Ġseparate Ġmodels Ġfor Ġeach Ġlanguage Ġrequires Ġboth Ġlabeled Ġand Ġunl abel ed Ġdata . ĠEven Ġthough Ġhaving Ġlots Ġof Ġlabeled Ġdata Ġin Ġevery Ġlanguage Ġis Ġthe Ġperfect Ġcase , Ġit Ġis Ġunrealistic . ĠTherefore , Ġeliminating Ġthe Ġresource Ġrequirement Ġin Ġthis Ġresource - con str ained Ġtask Ġis Ġcrucial . ĠThe Ġfact Ġthat Ġmachine Ġtranslation Ġcan Ġbe Ġused Ġin Ġre using Ġmodels Ġfrom Ġdifferent Ġlanguages Ġis Ġpromising Ġfor Ġreducing Ġthe Ġdata Ġrequirements . </s> Conclusion </s> Building Ġeffective Ġmachine Ġlearning Ġmodels Ġfor Ġtext Ġrequires Ġdata Ġand Ġdifferent Ġresources Ġsuch Ġas Ġpre - trained Ġword Ġembed d ings Ġand Ġreusable Ġlex icons . ĠUnfortunately , Ġmost Ġof Ġthese Ġresources Ġare Ġnot Ġentirely Ġtransfer able Ġto Ġdifferent Ġdomains , Ġtasks Ġor Ġlanguages . ĠSent iment Ġanalysis Ġis Ġone Ġsuch Ġtask Ġthat Ġrequires Ġadditional Ġeffort Ġto Ġtransfer Ġknowledge Ġbetween Ġlanguages . </s> In Ġthis Ġpaper , Ġwe Ġstudied Ġthe Ġresearch Ġquestion : ĠCan Ġwe Ġbuild Ġreusable Ġsentiment Ġanalysis Ġmodels Ġthat Ġcan Ġbe Ġutilized Ġfor Ġmaking Ġinf erences Ġin Ġdifferent Ġlanguages Ġwithout Ġrequiring Ġseparate Ġmodels Ġand Ġresources Ġfor Ġeach Ġlanguage ? ĠTo Ġthat Ġend , Ġwe Ġbuilt Ġa Ġrecurrent Ġneural Ġnetwork Ġmodel Ġin Ġthe Ġlanguage Ġthat Ġhad Ġlargest Ġdata Ġavailable . ĠWe Ġtook Ġa Ġgeneral - to - specific Ġmodel Ġbuilding Ġstrategy Ġwhere Ġthe Ġlarger Ġcorpus Ġthat Ġhad Ġreviews Ġfrom Ġdifferent Ġdomains Ġwas Ġfirst Ġused Ġto Ġtrain Ġthe ĠR NN Ġmodel Ġand Ġa Ġsmaller Ġsingle - domain Ġcorpus Ġof Ġsentiment Ġreviews Ġwas Ġused Ġto Ġspecialize Ġthe Ġmodel Ġon Ġthe Ġgiven Ġdomain . ĠDuring Ġscoring Ġtime , Ġwe Ġused Ġcorpor a Ġfor Ġthe Ġgiven Ġdomain Ġin Ġdifferent Ġlanguages Ġand Ġtranslated Ġthem Ġto ĠEnglish Ġto Ġbe Ġable Ġto Ġclassify Ġsentiments Ġwith Ġthe Ġtrained Ġmodel . ĠExperimental Ġresults Ġshowed Ġthat Ġthe Ġproposed Ġmult ilingual Ġapproach Ġoutper forms Ġboth Ġthe Ġmajority Ġbaseline Ġand Ġthe Ġlex icon - based Ġbaseline . </s> In Ġthis Ġpaper Ġwe Ġmade Ġthe Ġsentiment Ġanalysis Ġmodel Ġspecific Ġto Ġa Ġsingle Ġdomain . ĠFor Ġfuture Ġwork , Ġwe Ġwould Ġlike Ġto Ġinvestigate Ġthe Ġeffectiveness Ġof Ġour Ġmodel Ġon Ġdifferent Ġreview Ġdomains Ġincluding Ġhotel Ġreviews Ġand Ġon Ġdifferent Ġproblems Ġsuch Ġas Ġdetecting Ġstance .
Introduction With the steady growth in the commercial websites and social media venues, the access to users' reviews have become easier. As the amount of data that can be mined for opinion increased, commercial companies' interests for sentiment analysis increased as well. Sentiment analysis is an important part of understanding user behavior and opinions on products, places, or services. Sentiment analysis has long been studied by the research community, leading to several sentiment-related resources such as sentiment dictionaries that can be used as features for machine learning models BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . These resources help increase sentiment analysis accuracies; however, they are highly dependent on language and require researchers to build such resources for every language to process. Feature engineering is a large part of the model building phase for most sentiment analysis and emotion detection models BIBREF4 . Determining the correct set of features is a task that requires thorough investigation. Furthermore, these features are mostly language and dataset dependent making it even further challenging to build models for different languages. For example, the sentiment and emotion lexicons, as well as pre-trained word embeddings are not completely transferable to other languages which replicates the efforts for every language that users would like to build sentiment classification models on. For languages and tasks where the data is limited, extracting these features, building language models, training word embeddings, and creating lexicons are big challenges. In addition to the feature engineering effort, the machine learning models' parameters also need to be tuned separately for each language to get the optimal results. In this paper, we take a different approach. We build a reusable sentiment analysis model that does not utilize any lexicons. Our goal is to evaluate how well a generic model can be used to mine opinion in different languages where data is more limited than the language where the generic model is trained on. To that end, we build a training set that contains reviews from different domains in English (e.g., movie reviews, product reviews) and train a recurrent neural network (RNN) model to predict polarity of those reviews. Then focusing on a domain, we make the model specialized in that domain by using the trained weights from the larger data and further training with data on a specific domain. To evaluate the reusability of the sentiment analysis model, we test with non-English datasets. We first translate the test set to English and use the pre-trained model to score polarity in the translated text. In this way, our proposed approach eliminates the need to train language-dependent models, use of sentiment lexicons and word embeddings for each language. Our experiments show that a generalizable sentiment analysis model can be utilized successfully to perform opinion mining for languages that do not have enough resources to train specific models. The contributions of this study are; 1) a robust approach that utilizes machine translation to reuse a model trained on one language in other languages, 2) an RNN-based approach to eliminate feature extraction as well as resource requirements for sentiment analysis, and 3) a technique that statistically significantly outperforms baselines for multilingual sentiment analysis task when data is limited. To the best of our knowledge, this study is the first to apply a deep learning model to the multilingual sentiment analysis task. Related Work There is a rich body of work in sentiment analysis including social media platforms such as Twitter BIBREF5 and Facebook BIBREF4 . One common factor in most of the sentiment analysis work is that features that are specific to sentiment analysis are extracted (e.g., sentiment lexicons) and used in different machine learning models. Lexical resources BIBREF0 , BIBREF1 , BIBREF4 for sentiment analysis such as SentiWordNet BIBREF6 , BIBREF7 , linguistic features and expressions BIBREF8 , polarity dictionaries BIBREF2 , BIBREF3 , other features such as topic-oriented features and syntax BIBREF9 , emotion tokens BIBREF10 , word vectors BIBREF11 , and emographics BIBREF12 are some of the information that are found useful for improving sentiment analysis accuracies. Although these features are beneficial, extracting them requires language-dependent data (e.g., a sentiment dictionary for Spanish is trained on Spanish data instead of using all data from different languages). Our goal in this work is to streamline the feature engineering phase by not relying on any dictionary other than English word embeddings that are trained on any data (i.e. not necessarily sentiment analysis corpus). To that end, we utilize off-the-shelf machine translation tools to first translate corpora to the language where more training data is available and use the translated corpora to do inference on. Machine translation for multilingual sentiment analysis has also seen attention from researchers. Hiroshi et al. BIBREF13 translated only sentiment units with a pattern-based approach. Balahur and Turchi BIBREF14 used uni-grams, bi-grams and tf-idf features for building support vector machines on translated text. Boyd-Graber and Resnik BIBREF15 built Latent Dirichlet Allocation models to investigate how multilingual concepts are clustered into topics. Mohammed et al. BIBREF16 translate Twitter posts to English as well as the English sentiment lexicons. Tellez et al. BIBREF17 propose a framework where language-dependent and independent features are used with an SVM classifier. These machine learning approaches also require a feature extraction phase where we eliminate by incorporating a deep learning approach that does the feature learning intrinsically. Further, Wan BIBREF18 uses an ensemble approach where the resources (e.g., lexicons) in both the original language and the translated language are used – requiring resources to be present in both languages. Brooke et al. BIBREF19 also use multiple dictionaries. In this paper, we address the resource bottleneck of these translation-based approaches and propose a deep learning approach that does not require any dictionaries. Methodology In order to eliminate the need to find data and build separate models for each language, we propose a multilingual approach where a single model is built in the language where the largest resources are available. In this paper we focus on English as there are several sentiment analysis datasets in English. To make the English sentiment analysis model as generalizable as possible, we first start by training with a large dataset that has product reviews for different categories. Then, using the trained weights from the larger generic dataset, we make the model more specialized for a specific domain. We further train the model with domain-specific English reviews and use this trained model to score reviews that share the same domain from different languages. To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place. Figure FIGREF1 shows our multilingual sentiment analysis approach. It is important to note that this approach does not utilize any resource in any of the languages of the test sets (e.g., word embeddings, lexicons, training set). Deep learning approaches have been successful in many applications ranging from computer vision to natural language processing BIBREF20 . Recurrent neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are subsets of deep learning algorithms where the dependencies between tokens can be used by the model. These models can also be used with variable length input vectors which makes them suitable for text input. LSTM and GRU models allow operations of sequences of vectors over time and have the capability to `remember' previous information BIBREF20 . RNN have been found useful for several natural language processing tasks including language modeling, text classification, machine translation. RNN can also utilize pre-trained word embeddings (numeric vector representations of words trained on unlabeled data) without requiring hand-crafted features. Therefore in this paper, we employ an RNN architecture that takes text and pre-trained word embeddings as inputs and generates a classification result. Word embeddings represent words as numeric vectors and capture semantic information. They are trained in an unsupervised fashion making it useful for our task. The sentiment analysis model that is trained on English reviews has two bidirectional layers, each with 40 neurons and a dropout BIBREF21 of 0.2 is used. The training phase takes pre-trained word embeddings and reviews in textual format, then predicts the polarity of the reviews. For this study, an embedding length of 100 is used (i.e., each word is represented by a vector of length 100). We utilized pre-trained global vectors BIBREF22 . The training phase is depicted in Figure FIGREF2 . Experiments To evaluate the proposed approach for multilingual sentiment analysis task, we conducted experiments. This section first presents the corpora used in this study followed by experimental results. Throughout our experiments, we use SAS Deep Learning Toolkit. For machine translation, Google translation API is used. Corpora Two sets of corpora are used in this study, both are publicly available. The first set consists of English reviews and the second set contains restaurant reviews from four different languages (Spanish, Turkish, Dutch, Russian). We focus on polarity detection in reviews, therefore all datasets in this study have two class values (positive, negative). With the goal of building a generalizable sentiment analysis model, we used three different training sets as provided in Table TABREF5 . One of these three datasets (Amazon reviews BIBREF23 , BIBREF24 ) is larger and has product reviews from several different categories including book reviews, electronics products reviews, and application reviews. The other two datasets are to make the model more specialized in the domain. In this paper we focus on restaurant reviews as our domain and use Yelp restaurant reviews dataset extracted from Yelp Dataset Challenge BIBREF25 and restaurant reviews dataset as part of a Kaggle competition BIBREF26 . For evaluation of the multilingual approach, we use four languages. These datasets are part of SemEval-2016 Challenge Task 5 BIBREF27 , BIBREF28 . Table TABREF7 shows the number of observations in each test corpus. Experimental Results For experimental results, we report majority baseline for each language where the majority baseline corresponds to a model's accuracy if it always predicts the majority class in the dataset. For example, if the dataset has 60% of all reviews positive and 40% negative, majority baseline would be 60% because a model that always predicts “positive” will be 60% accurate and will make mistakes 40% of the time. In addition to the majority baseline, we also compare our results with a lexicon-based approach. We use SentiWordNet BIBREF29 to obtain a positive and a negative sentiment score for each token in a review. Then sum of positive sentiment scores and negative sentiment scores for each review is obtained by summing up the scores for each token. If the positive sum score for a given review is greater than the negative sum score, we accept that review as a positive review. If negative sum is larger than or equal to the positive sum, the review is labeled as a negative review. RNN outperforms both baselines in all four datasets (see Table TABREF9 ). Also for Spanish restaurant review, the lexicon-based baseline is below the majority baseline which shows that solely translating data and using lexicons is not sufficient to achieve good results in multilingual sentiment analysis. Among the wrong classifications for each test set, we calculated the percentage of false positives and false negatives. Table TABREF10 shows the distribution of false positives and false negatives for each class. In all four classes, the number of false negatives are more than the number of false positives. This can be explained by the unbalanced training dataset where the number of positive reviews are more than the number of negative reviews (59,577 vs 17,132). To be able to see the difference between baseline and RNN, we took each method's results as a group (4 values: one for each language) and compared the means. Post hoc comparisons using the Tukey HSD test indicated that the mean accuracies for baselines (majority and lexicon-based) are significantly different than RNN accuracies as can be seen in Table TABREF12 (family-wise error rate=0.06). When RNN is compared with lexicon-based baseline and majority baseline, the null hypothesis can be rejected meaning that each test is significant. In addition to these comparisons, we also calculated the effect sizes (using Cohen's d) between the baselines and our method. The results are aligning with Tukey HSD results such that while our method versus baselines have very large effect sizes, lexicon-based baseline and majority baseline have negligible effect size. Figure FIGREF11 shows the differences in minimum and maximum values of all three approaches. As the figure shows, RNN significantly outperforms both baselines for the sentiment classification task. Discussion One of the crucial elements while using machine translation is to have highly accurate translations. It is likely that non-English words would not have word embeddings, which will dramatically affect the effectiveness of the system. We analyzed the effect of incorrect translations into our approach. To that end, we extracted all wrong predictions from the test set and computed the ratio of misclassifications that have non-English words in them. We first extracted all misclassifications for a given language and for each observation in the misclassification set, we iterated through each token to check if the token is in English. In this way, we counted the number of observations that contained at least one non-English word and divided that with the size of the misclassifications set. We used this ratio to investigate the effect of machine translation errors. We found that 25.84% of Dutch, 21.76% of Turkish, 24.46% Spanish, and 10.71% of Russian reviews that were misclassified had non-English words in them. These non-English words might be causing the misclassifications. However, a large portion of the missclassifications is not caused due to not-translated words. At the end, the machine translation errors has some but not noticeable effects on our model. Therefore, we can claim that machine translation preserves most of the information necessary for sentiment analysis. We also evaluated our model with an English corpus BIBREF27 to see its performance without any interference from machine translation errors. Using the English data for testing, the model achieved 87.06% accuracy where a majority baseline was 68.37% and the lexicon-based baseline was 60.10%. Considering the improvements over the majority baseline achieved by the RNN model for both non-English (on the average 22.76% relative improvement; 15.82% relative improvement on Spanish, 72.71% vs. 84.21%, 30.53% relative improvement on Turkish, 56.97% vs. 74.36%, 37.13% relative improvement on Dutch, 59.63% vs. 81.77%, and 7.55% relative improvement on Russian, 79.60% vs. 85.62%) and English test sets (27.34% relative improvement), we can draw the conclusion that our model is robust to handle multiple languages. Building separate models for each language requires both labeled and unlabeled data. Even though having lots of labeled data in every language is the perfect case, it is unrealistic. Therefore, eliminating the resource requirement in this resource-constrained task is crucial. The fact that machine translation can be used in reusing models from different languages is promising for reducing the data requirements. Conclusion Building effective machine learning models for text requires data and different resources such as pre-trained word embeddings and reusable lexicons. Unfortunately, most of these resources are not entirely transferable to different domains, tasks or languages. Sentiment analysis is one such task that requires additional effort to transfer knowledge between languages. In this paper, we studied the research question: Can we build reusable sentiment analysis models that can be utilized for making inferences in different languages without requiring separate models and resources for each language? To that end, we built a recurrent neural network model in the language that had largest data available. We took a general-to-specific model building strategy where the larger corpus that had reviews from different domains was first used to train the RNN model and a smaller single-domain corpus of sentiment reviews was used to specialize the model on the given domain. During scoring time, we used corpora for the given domain in different languages and translated them to English to be able to classify sentiments with the trained model. Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline. In this paper we made the sentiment analysis model specific to a single domain. For future work, we would like to investigate the effectiveness of our model on different review domains including hotel reviews and on different problems such as detecting stance.
which non-english language had the best performance?
e79a5b6b6680bd2f63e9f4adbaae1d7795d81e38
which Ġnon - english Ġlanguage Ġhad Ġthe Ġbest Ġperformance ?
Russian
<s> which Ġnon - english Ġlanguage Ġhad Ġthe Ġbest Ġperformance ? </s> Introduction </s> With Ġthe Ġsteady Ġgrowth Ġin Ġthe Ġcommercial Ġwebsites Ġand Ġsocial Ġmedia Ġvenues , Ġthe Ġaccess Ġto Ġusers ' Ġreviews Ġhave Ġbecome Ġeasier . ĠAs Ġthe Ġamount Ġof Ġdata Ġthat Ġcan Ġbe Ġmined Ġfor Ġopinion Ġincreased , Ġcommercial Ġcompanies ' Ġinterests Ġfor Ġsentiment Ġanalysis Ġincreased Ġas Ġwell . ĠSent iment Ġanalysis Ġis Ġan Ġimportant Ġpart Ġof Ġunderstanding Ġuser Ġbehavior Ġand Ġopinions Ġon Ġproducts , Ġplaces , Ġor Ġservices . </s> Sent iment Ġanalysis Ġhas Ġlong Ġbeen Ġstudied Ġby Ġthe Ġresearch Ġcommunity , Ġleading Ġto Ġseveral Ġsentiment - related Ġresources Ġsuch Ġas Ġsentiment Ġdiction aries Ġthat Ġcan Ġbe Ġused Ġas Ġfeatures Ġfor Ġmachine Ġlearning Ġmodels ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ. ĠThese Ġresources Ġhelp Ġincrease Ġsentiment Ġanalysis Ġaccur acies ; Ġhowever , Ġthey Ġare Ġhighly Ġdependent Ġon Ġlanguage Ġand Ġrequire Ġresearchers Ġto Ġbuild Ġsuch Ġresources Ġfor Ġevery Ġlanguage Ġto Ġprocess . </s> Feature Ġengineering Ġis Ġa Ġlarge Ġpart Ġof Ġthe Ġmodel Ġbuilding Ġphase Ġfor Ġmost Ġsentiment Ġanalysis Ġand Ġemotion Ġdetection Ġmodels ĠB IB REF 4 Ġ. ĠD etermin ing Ġthe Ġcorrect Ġset Ġof Ġfeatures Ġis Ġa Ġtask Ġthat Ġrequires Ġthorough Ġinvestigation . ĠFurthermore , Ġthese Ġfeatures Ġare Ġmostly Ġlanguage Ġand Ġdataset Ġdependent Ġmaking Ġit Ġeven Ġfurther Ġchallenging Ġto Ġbuild Ġmodels Ġfor Ġdifferent Ġlanguages . ĠFor Ġexample , Ġthe Ġsentiment Ġand Ġemotion Ġlex icons , Ġas Ġwell Ġas Ġpre - trained Ġword Ġembed d ings Ġare Ġnot Ġcompletely Ġtransfer able Ġto Ġother Ġlanguages Ġwhich Ġrepl icates Ġthe Ġefforts Ġfor Ġevery Ġlanguage Ġthat Ġusers Ġwould Ġlike Ġto Ġbuild Ġsentiment Ġclassification Ġmodels Ġon . ĠFor Ġlanguages Ġand Ġtasks Ġwhere Ġthe Ġdata Ġis Ġlimited , Ġextracting Ġthese Ġfeatures , Ġbuilding Ġlanguage Ġmodels , Ġtraining Ġword Ġembed d ings , Ġand Ġcreating Ġlex icons Ġare Ġbig Ġchallenges . ĠIn Ġaddition Ġto Ġthe Ġfeature Ġengineering Ġeffort , Ġthe Ġmachine Ġlearning Ġmodels ' Ġparameters Ġalso Ġneed Ġto Ġbe Ġtuned Ġseparately Ġfor Ġeach Ġlanguage Ġto Ġget Ġthe Ġoptimal Ġresults . </s> In Ġthis Ġpaper , Ġwe Ġtake Ġa Ġdifferent Ġapproach . ĠWe Ġbuild Ġa Ġreusable Ġsentiment Ġanalysis Ġmodel Ġthat Ġdoes Ġnot Ġutilize Ġany Ġlex icons . ĠOur Ġgoal Ġis Ġto Ġevaluate Ġhow Ġwell Ġa Ġgeneric Ġmodel Ġcan Ġbe Ġused Ġto Ġmine Ġopinion Ġin Ġdifferent Ġlanguages Ġwhere Ġdata Ġis Ġmore Ġlimited Ġthan Ġthe Ġlanguage Ġwhere Ġthe Ġgeneric Ġmodel Ġis Ġtrained Ġon . ĠTo Ġthat Ġend , Ġwe Ġbuild Ġa Ġtraining Ġset Ġthat Ġcontains Ġreviews Ġfrom Ġdifferent Ġdomains Ġin ĠEnglish Ġ( e . g ., Ġmovie Ġreviews , Ġproduct Ġreviews ) Ġand Ġtrain Ġa Ġrecurrent Ġneural Ġnetwork Ġ( R NN ) Ġmodel Ġto Ġpredict Ġpol arity Ġof Ġthose Ġreviews . ĠThen Ġfocusing Ġon Ġa Ġdomain , Ġwe Ġmake Ġthe Ġmodel Ġspecialized Ġin Ġthat Ġdomain Ġby Ġusing Ġthe Ġtrained Ġweights Ġfrom Ġthe Ġlarger Ġdata Ġand Ġfurther Ġtraining Ġwith Ġdata Ġon Ġa Ġspecific Ġdomain . ĠTo Ġevaluate Ġthe Ġre us ability Ġof Ġthe Ġsentiment Ġanalysis Ġmodel , Ġwe Ġtest Ġwith Ġnon - English Ġdatasets . ĠWe Ġfirst Ġtranslate Ġthe Ġtest Ġset Ġto ĠEnglish Ġand Ġuse Ġthe Ġpre - trained Ġmodel Ġto Ġscore Ġpol arity Ġin Ġthe Ġtranslated Ġtext . ĠIn Ġthis Ġway , Ġour Ġproposed Ġapproach Ġeliminates Ġthe Ġneed Ġto Ġtrain Ġlanguage - dependent Ġmodels , Ġuse Ġof Ġsentiment Ġlex icons Ġand Ġword Ġembed d ings Ġfor Ġeach Ġlanguage . ĠOur Ġexperiments Ġshow Ġthat Ġa Ġgeneral izable Ġsentiment Ġanalysis Ġmodel Ġcan Ġbe Ġutilized Ġsuccessfully Ġto Ġperform Ġopinion Ġmining Ġfor Ġlanguages Ġthat Ġdo Ġnot Ġhave Ġenough Ġresources Ġto Ġtrain Ġspecific Ġmodels . </s> The Ġcontributions Ġof Ġthis Ġstudy Ġare ; Ġ1 ) Ġa Ġrobust Ġapproach Ġthat Ġutilizes Ġmachine Ġtranslation Ġto Ġreuse Ġa Ġmodel Ġtrained Ġon Ġone Ġlanguage Ġin Ġother Ġlanguages , Ġ2 ) Ġan ĠR NN - based Ġapproach Ġto Ġeliminate Ġfeature Ġextraction Ġas Ġwell Ġas Ġresource Ġrequirements Ġfor Ġsentiment Ġanalysis , Ġand Ġ3 ) Ġa Ġtechnique Ġthat Ġstatistically Ġsignificantly Ġoutper forms Ġbas elines Ġfor Ġmult ilingual Ġsentiment Ġanalysis Ġtask Ġwhen Ġdata Ġis Ġlimited . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthis Ġstudy Ġis Ġthe Ġfirst Ġto Ġapply Ġa Ġdeep Ġlearning Ġmodel Ġto Ġthe Ġmult ilingual Ġsentiment Ġanalysis Ġtask . </s> Related ĠWork </s> There Ġis Ġa Ġrich Ġbody Ġof Ġwork Ġin Ġsentiment Ġanalysis Ġincluding Ġsocial Ġmedia Ġplatforms Ġsuch Ġas ĠTwitter ĠB IB REF 5 Ġand ĠFacebook ĠB IB REF 4 Ġ. ĠOne Ġcommon Ġfactor Ġin Ġmost Ġof Ġthe Ġsentiment Ġanalysis Ġwork Ġis Ġthat Ġfeatures Ġthat Ġare Ġspecific Ġto Ġsentiment Ġanalysis Ġare Ġextracted Ġ( e . g ., Ġsentiment Ġlex icons ) Ġand Ġused Ġin Ġdifferent Ġmachine Ġlearning Ġmodels . ĠLex ical Ġresources ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 4 Ġfor Ġsentiment Ġanalysis Ġsuch Ġas ĠSent i Word Net ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, Ġlinguistic Ġfeatures Ġand Ġexpressions ĠB IB REF 8 Ġ, Ġpol arity Ġdiction aries ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ, Ġother Ġfeatures Ġsuch Ġas Ġtopic - oriented Ġfeatures Ġand Ġsyntax ĠB IB REF 9 Ġ, Ġemotion Ġtokens ĠB IB REF 10 Ġ, Ġword Ġvectors ĠB IB REF 11 Ġ, Ġand Ġem ographics ĠB IB REF 12 Ġare Ġsome Ġof Ġthe Ġinformation Ġthat Ġare Ġfound Ġuseful Ġfor Ġimproving Ġsentiment Ġanalysis Ġaccur acies . ĠAlthough Ġthese Ġfeatures Ġare Ġbeneficial , Ġextracting Ġthem Ġrequires Ġlanguage - dependent Ġdata Ġ( e . g ., Ġa Ġsentiment Ġdictionary Ġfor ĠSpanish Ġis Ġtrained Ġon ĠSpanish Ġdata Ġinstead Ġof Ġusing Ġall Ġdata Ġfrom Ġdifferent Ġlanguages ). </s> Our Ġgoal Ġin Ġthis Ġwork Ġis Ġto Ġstream line Ġthe Ġfeature Ġengineering Ġphase Ġby Ġnot Ġrelying Ġon Ġany Ġdictionary Ġother Ġthan ĠEnglish Ġword Ġembed d ings Ġthat Ġare Ġtrained Ġon Ġany Ġdata Ġ( i . e . Ġnot Ġnecessarily Ġsentiment Ġanalysis Ġcorpus ). ĠTo Ġthat Ġend , Ġwe Ġutilize Ġoff - the - she lf Ġmachine Ġtranslation Ġtools Ġto Ġfirst Ġtranslate Ġcorpor a Ġto Ġthe Ġlanguage Ġwhere Ġmore Ġtraining Ġdata Ġis Ġavailable Ġand Ġuse Ġthe Ġtranslated Ġcorpor a Ġto Ġdo Ġinference Ġon . </s> Machine Ġtranslation Ġfor Ġmult ilingual Ġsentiment Ġanalysis Ġhas Ġalso Ġseen Ġattention Ġfrom Ġresearchers . ĠHirosh i Ġet Ġal . ĠB IB REF 13 Ġtranslated Ġonly Ġsentiment Ġunits Ġwith Ġa Ġpattern - based Ġapproach . ĠBal ah ur Ġand ĠT urch i ĠB IB REF 14 Ġused Ġun i - gram s , Ġbi - gram s Ġand Ġtf - id f Ġfeatures Ġfor Ġbuilding Ġsupport Ġvector Ġmachines Ġon Ġtranslated Ġtext . ĠBoyd - Gr aber Ġand ĠRes nik ĠB IB REF 15 Ġbuilt ĠLat ent ĠDir ich let ĠAll ocation Ġmodels Ġto Ġinvestigate Ġhow Ġmult ilingual Ġconcepts Ġare Ġclustered Ġinto Ġtopics . ĠMohammed Ġet Ġal . ĠB IB REF 16 Ġtranslate ĠTwitter Ġposts Ġto ĠEnglish Ġas Ġwell Ġas Ġthe ĠEnglish Ġsentiment Ġlex icons . ĠTel lez Ġet Ġal . ĠB IB REF 17 Ġpropose Ġa Ġframework Ġwhere Ġlanguage - dependent Ġand Ġindependent Ġfeatures Ġare Ġused Ġwith Ġan ĠS VM Ġclass ifier . ĠThese Ġmachine Ġlearning Ġapproaches Ġalso Ġrequire Ġa Ġfeature Ġextraction Ġphase Ġwhere Ġwe Ġeliminate Ġby Ġincorporating Ġa Ġdeep Ġlearning Ġapproach Ġthat Ġdoes Ġthe Ġfeature Ġlearning Ġintrinsically . ĠFurther , ĠWan ĠB IB REF 18 Ġuses Ġan Ġensemble Ġapproach Ġwhere Ġthe Ġresources Ġ( e . g ., Ġlex icons ) Ġin Ġboth Ġthe Ġoriginal Ġlanguage Ġand Ġthe Ġtranslated Ġlanguage Ġare Ġused ĠâĢĵ Ġrequiring Ġresources Ġto Ġbe Ġpresent Ġin Ġboth Ġlanguages . ĠBrooke Ġet Ġal . ĠB IB REF 19 Ġalso Ġuse Ġmultiple Ġdiction aries . </s> In Ġthis Ġpaper , Ġwe Ġaddress Ġthe Ġresource Ġbottleneck Ġof Ġthese Ġtranslation - based Ġapproaches Ġand Ġpropose Ġa Ġdeep Ġlearning Ġapproach Ġthat Ġdoes Ġnot Ġrequire Ġany Ġdiction aries . </s> Method ology </s> In Ġorder Ġto Ġeliminate Ġthe Ġneed Ġto Ġfind Ġdata Ġand Ġbuild Ġseparate Ġmodels Ġfor Ġeach Ġlanguage , Ġwe Ġpropose Ġa Ġmult ilingual Ġapproach Ġwhere Ġa Ġsingle Ġmodel Ġis Ġbuilt Ġin Ġthe Ġlanguage Ġwhere Ġthe Ġlargest Ġresources Ġare Ġavailable . ĠIn Ġthis Ġpaper Ġwe Ġfocus Ġon ĠEnglish Ġas Ġthere Ġare Ġseveral Ġsentiment Ġanalysis Ġdatasets Ġin ĠEnglish . ĠTo Ġmake Ġthe ĠEnglish Ġsentiment Ġanalysis Ġmodel Ġas Ġgeneral izable Ġas Ġpossible , Ġwe Ġfirst Ġstart Ġby Ġtraining Ġwith Ġa Ġlarge Ġdataset Ġthat Ġhas Ġproduct Ġreviews Ġfor Ġdifferent Ġcategories . ĠThen , Ġusing Ġthe Ġtrained Ġweights Ġfrom Ġthe Ġlarger Ġgeneric Ġdataset , Ġwe Ġmake Ġthe Ġmodel Ġmore Ġspecialized Ġfor Ġa Ġspecific Ġdomain . ĠWe Ġfurther Ġtrain Ġthe Ġmodel Ġwith Ġdomain - specific ĠEnglish Ġreviews Ġand Ġuse Ġthis Ġtrained Ġmodel Ġto Ġscore Ġreviews Ġthat Ġshare Ġthe Ġsame Ġdomain Ġfrom Ġdifferent Ġlanguages . ĠTo Ġbe Ġable Ġto Ġemploy Ġthe Ġtrained Ġmodel , Ġtest Ġsets Ġare Ġfirst Ġtranslated Ġto ĠEnglish Ġvia Ġmachine Ġtranslation Ġand Ġthen Ġinference Ġtakes Ġplace . ĠFigure ĠFIG REF 1 Ġshows Ġour Ġmult ilingual Ġsentiment Ġanalysis Ġapproach . ĠIt Ġis Ġimportant Ġto Ġnote Ġthat Ġthis Ġapproach Ġdoes Ġnot Ġutilize Ġany Ġresource Ġin Ġany Ġof Ġthe Ġlanguages Ġof Ġthe Ġtest Ġsets Ġ( e . g ., Ġword Ġembed d ings , Ġlex icons , Ġtraining Ġset ). </s> Deep Ġlearning Ġapproaches Ġhave Ġbeen Ġsuccessful Ġin Ġmany Ġapplications Ġranging Ġfrom Ġcomputer Ġvision Ġto Ġnatural Ġlanguage Ġprocessing ĠB IB REF 20 Ġ. ĠRec urrent Ġneural Ġnetwork Ġ( R NN ) Ġincluding ĠLong ĠShort ĠTerm ĠMemory Ġ( L ST M ) Ġand ĠG ated ĠRec urrent ĠUnits Ġ( GR U ) Ġare Ġsubs ets Ġof Ġdeep Ġlearning Ġalgorithms Ġwhere Ġthe Ġdependencies Ġbetween Ġtokens Ġcan Ġbe Ġused Ġby Ġthe Ġmodel . ĠThese Ġmodels Ġcan Ġalso Ġbe Ġused Ġwith Ġvariable Ġlength Ġinput Ġvectors Ġwhich Ġmakes Ġthem Ġsuitable Ġfor Ġtext Ġinput . ĠL ST M Ġand ĠGR U Ġmodels Ġallow Ġoperations Ġof Ġsequences Ġof Ġvectors Ġover Ġtime Ġand Ġhave Ġthe Ġcapability Ġto Ġ` remember ' Ġprevious Ġinformation ĠB IB REF 20 Ġ. ĠR NN Ġhave Ġbeen Ġfound Ġuseful Ġfor Ġseveral Ġnatural Ġlanguage Ġprocessing Ġtasks Ġincluding Ġlanguage Ġmodeling , Ġtext Ġclassification , Ġmachine Ġtranslation . ĠR NN Ġcan Ġalso Ġutilize Ġpre - trained Ġword Ġembed d ings Ġ( n umeric Ġvector Ġrepresentations Ġof Ġwords Ġtrained Ġon Ġunl abel ed Ġdata ) Ġwithout Ġrequiring Ġhand - crafted Ġfeatures . ĠTherefore Ġin Ġthis Ġpaper , Ġwe Ġemploy Ġan ĠR NN Ġarchitecture Ġthat Ġtakes Ġtext Ġand Ġpre - trained Ġword Ġembed d ings Ġas Ġinputs Ġand Ġgenerates Ġa Ġclassification Ġresult . ĠWord Ġembed d ings Ġrepresent Ġwords Ġas Ġnumeric Ġvectors Ġand Ġcapture Ġsemantic Ġinformation . ĠThey Ġare Ġtrained Ġin Ġan Ġun super vised Ġfashion Ġmaking Ġit Ġuseful Ġfor Ġour Ġtask . </s> The Ġsentiment Ġanalysis Ġmodel Ġthat Ġis Ġtrained Ġon ĠEnglish Ġreviews Ġhas Ġtwo Ġbid irection al Ġlayers , Ġeach Ġwith Ġ40 Ġneurons Ġand Ġa Ġdrop out ĠB IB REF 21 Ġof Ġ0 . 2 Ġis Ġused . ĠThe Ġtraining Ġphase Ġtakes Ġpre - trained Ġword Ġembed d ings Ġand Ġreviews Ġin Ġtextual Ġformat , Ġthen Ġpredicts Ġthe Ġpol arity Ġof Ġthe Ġreviews . ĠFor Ġthis Ġstudy , Ġan Ġembed ding Ġlength Ġof Ġ100 Ġis Ġused Ġ( i . e ., Ġeach Ġword Ġis Ġrepresented Ġby Ġa Ġvector Ġof Ġlength Ġ100 ). ĠWe Ġutilized Ġpre - trained Ġglobal Ġvectors ĠB IB REF 22 Ġ. ĠThe Ġtraining Ġphase Ġis Ġdepicted Ġin ĠFigure ĠFIG REF 2 Ġ. </s> Exper iments </s> To Ġevaluate Ġthe Ġproposed Ġapproach Ġfor Ġmult ilingual Ġsentiment Ġanalysis Ġtask , Ġwe Ġconducted Ġexperiments . ĠThis Ġsection Ġfirst Ġpresents Ġthe Ġcorpor a Ġused Ġin Ġthis Ġstudy Ġfollowed Ġby Ġexperimental Ġresults . </s> Throughout Ġour Ġexperiments , Ġwe Ġuse ĠSAS ĠDeep ĠLearning ĠTool kit . ĠFor Ġmachine Ġtranslation , ĠGoogle Ġtranslation ĠAPI Ġis Ġused . </s> Cor pora </s> Two Ġsets Ġof Ġcorpor a Ġare Ġused Ġin Ġthis Ġstudy , Ġboth Ġare Ġpublicly Ġavailable . ĠThe Ġfirst Ġset Ġconsists Ġof ĠEnglish Ġreviews Ġand Ġthe Ġsecond Ġset Ġcontains Ġrestaurant Ġreviews Ġfrom Ġfour Ġdifferent Ġlanguages Ġ( Spanish , ĠTurkish , ĠDutch , ĠRussian ). ĠWe Ġfocus Ġon Ġpol arity Ġdetection Ġin Ġreviews , Ġtherefore Ġall Ġdatasets Ġin Ġthis Ġstudy Ġhave Ġtwo Ġclass Ġvalues Ġ( positive , Ġnegative ). </s> With Ġthe Ġgoal Ġof Ġbuilding Ġa Ġgeneral izable Ġsentiment Ġanalysis Ġmodel , Ġwe Ġused Ġthree Ġdifferent Ġtraining Ġsets Ġas Ġprovided Ġin ĠTable ĠT AB REF 5 Ġ. ĠOne Ġof Ġthese Ġthree Ġdatasets Ġ( Amazon Ġreviews ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ) Ġis Ġlarger Ġand Ġhas Ġproduct Ġreviews Ġfrom Ġseveral Ġdifferent Ġcategories Ġincluding Ġbook Ġreviews , Ġelectronics Ġproducts Ġreviews , Ġand Ġapplication Ġreviews . ĠThe Ġother Ġtwo Ġdatasets Ġare Ġto Ġmake Ġthe Ġmodel Ġmore Ġspecialized Ġin Ġthe Ġdomain . ĠIn Ġthis Ġpaper Ġwe Ġfocus Ġon Ġrestaurant Ġreviews Ġas Ġour Ġdomain Ġand Ġuse ĠYelp Ġrestaurant Ġreviews Ġdataset Ġextracted Ġfrom ĠYelp ĠDat as et ĠChallenge ĠB IB REF 25 Ġand Ġrestaurant Ġreviews Ġdataset Ġas Ġpart Ġof Ġa ĠK agg le Ġcompetition ĠB IB REF 26 Ġ. </s> For Ġevaluation Ġof Ġthe Ġmult ilingual Ġapproach , Ġwe Ġuse Ġfour Ġlanguages . ĠThese Ġdatasets Ġare Ġpart Ġof ĠSem E val - 2016 ĠChallenge ĠTask Ġ5 ĠB IB REF 27 Ġ, ĠB IB REF 28 Ġ. ĠTable ĠT AB REF 7 Ġshows Ġthe Ġnumber Ġof Ġobservations Ġin Ġeach Ġtest Ġcorpus . </s> Exper imental ĠResults </s> For Ġexperimental Ġresults , Ġwe Ġreport Ġmajority Ġbaseline Ġfor Ġeach Ġlanguage Ġwhere Ġthe Ġmajority Ġbaseline Ġcorresponds Ġto Ġa Ġmodel 's Ġaccuracy Ġif Ġit Ġalways Ġpredicts Ġthe Ġmajority Ġclass Ġin Ġthe Ġdataset . ĠFor Ġexample , Ġif Ġthe Ġdataset Ġhas Ġ60 % Ġof Ġall Ġreviews Ġpositive Ġand Ġ40 % Ġnegative , Ġmajority Ġbaseline Ġwould Ġbe Ġ60 % Ġbecause Ġa Ġmodel Ġthat Ġalways Ġpredicts ĠâĢ ľ positive âĢ Ŀ Ġwill Ġbe Ġ60 % Ġaccurate Ġand Ġwill Ġmake Ġmistakes Ġ40 % Ġof Ġthe Ġtime . </s> In Ġaddition Ġto Ġthe Ġmajority Ġbaseline , Ġwe Ġalso Ġcompare Ġour Ġresults Ġwith Ġa Ġlex icon - based Ġapproach . ĠWe Ġuse ĠSent i Word Net ĠB IB REF 29 Ġto Ġobtain Ġa Ġpositive Ġand Ġa Ġnegative Ġsentiment Ġscore Ġfor Ġeach Ġtoken Ġin Ġa Ġreview . ĠThen Ġsum Ġof Ġpositive Ġsentiment Ġscores Ġand Ġnegative Ġsentiment Ġscores Ġfor Ġeach Ġreview Ġis Ġobtained Ġby Ġsum ming Ġup Ġthe Ġscores Ġfor Ġeach Ġtoken . ĠIf Ġthe Ġpositive Ġsum Ġscore Ġfor Ġa Ġgiven Ġreview Ġis Ġgreater Ġthan Ġthe Ġnegative Ġsum Ġscore , Ġwe Ġaccept Ġthat Ġreview Ġas Ġa Ġpositive Ġreview . ĠIf Ġnegative Ġsum Ġis Ġlarger Ġthan Ġor Ġequal Ġto Ġthe Ġpositive Ġsum , Ġthe Ġreview Ġis Ġlabeled Ġas Ġa Ġnegative Ġreview . </s> R NN Ġoutper forms Ġboth Ġbas elines Ġin Ġall Ġfour Ġdatasets Ġ( see ĠTable ĠT AB REF 9 Ġ). ĠAlso Ġfor ĠSpanish Ġrestaurant Ġreview , Ġthe Ġlex icon - based Ġbaseline Ġis Ġbelow Ġthe Ġmajority Ġbaseline Ġwhich Ġshows Ġthat Ġsolely Ġtranslating Ġdata Ġand Ġusing Ġlex icons Ġis Ġnot Ġsufficient Ġto Ġachieve Ġgood Ġresults Ġin Ġmult ilingual Ġsentiment Ġanalysis . </s> Among Ġthe Ġwrong Ġclass ifications Ġfor Ġeach Ġtest Ġset , Ġwe Ġcalculated Ġthe Ġpercentage Ġof Ġfalse Ġpositives Ġand Ġfalse Ġnegatives . ĠTable ĠT AB REF 10 Ġshows Ġthe Ġdistribution Ġof Ġfalse Ġpositives Ġand Ġfalse Ġnegatives Ġfor Ġeach Ġclass . ĠIn Ġall Ġfour Ġclasses , Ġthe Ġnumber Ġof Ġfalse Ġnegatives Ġare Ġmore Ġthan Ġthe Ġnumber Ġof Ġfalse Ġpositives . ĠThis Ġcan Ġbe Ġexplained Ġby Ġthe Ġun balanced Ġtraining Ġdataset Ġwhere Ġthe Ġnumber Ġof Ġpositive Ġreviews Ġare Ġmore Ġthan Ġthe Ġnumber Ġof Ġnegative Ġreviews Ġ( 59 , 577 Ġvs Ġ17 , 132 ). </s> To Ġbe Ġable Ġto Ġsee Ġthe Ġdifference Ġbetween Ġbaseline Ġand ĠR NN , Ġwe Ġtook Ġeach Ġmethod 's Ġresults Ġas Ġa Ġgroup Ġ( 4 Ġvalues : Ġone Ġfor Ġeach Ġlanguage ) Ġand Ġcompared Ġthe Ġmeans . ĠPost Ġhoc Ġcomparisons Ġusing Ġthe ĠTu key ĠH SD Ġtest Ġindicated Ġthat Ġthe Ġmean Ġaccur acies Ġfor Ġbas elines Ġ( majority Ġand Ġlex icon - based ) Ġare Ġsignificantly Ġdifferent Ġthan ĠR NN Ġaccur acies Ġas Ġcan Ġbe Ġseen Ġin ĠTable ĠT AB REF 12 Ġ( family - wise Ġerror Ġrate = 0 . 06 ). ĠWhen ĠR NN Ġis Ġcompared Ġwith Ġlex icon - based Ġbaseline Ġand Ġmajority Ġbaseline , Ġthe Ġnull Ġhypothesis Ġcan Ġbe Ġrejected Ġmeaning Ġthat Ġeach Ġtest Ġis Ġsignificant . ĠIn Ġaddition Ġto Ġthese Ġcomparisons , Ġwe Ġalso Ġcalculated Ġthe Ġeffect Ġsizes Ġ( using ĠCohen 's Ġd ) Ġbetween Ġthe Ġbas elines Ġand Ġour Ġmethod . ĠThe Ġresults Ġare Ġalign ing Ġwith ĠTu key ĠH SD Ġresults Ġsuch Ġthat Ġwhile Ġour Ġmethod Ġversus Ġbas elines Ġhave Ġvery Ġlarge Ġeffect Ġsizes , Ġlex icon - based Ġbaseline Ġand Ġmajority Ġbaseline Ġhave Ġnegligible Ġeffect Ġsize . </s> Figure ĠFIG REF 11 Ġshows Ġthe Ġdifferences Ġin Ġminimum Ġand Ġmaximum Ġvalues Ġof Ġall Ġthree Ġapproaches . ĠAs Ġthe Ġfigure Ġshows , ĠR NN Ġsignificantly Ġoutper forms Ġboth Ġbas elines Ġfor Ġthe Ġsentiment Ġclassification Ġtask . </s> Discussion </s> One Ġof Ġthe Ġcrucial Ġelements Ġwhile Ġusing Ġmachine Ġtranslation Ġis Ġto Ġhave Ġhighly Ġaccurate Ġtranslations . ĠIt Ġis Ġlikely Ġthat Ġnon - English Ġwords Ġwould Ġnot Ġhave Ġword Ġembed d ings , Ġwhich Ġwill Ġdramatically Ġaffect Ġthe Ġeffectiveness Ġof Ġthe Ġsystem . ĠWe Ġanalyzed Ġthe Ġeffect Ġof Ġincorrect Ġtranslations Ġinto Ġour Ġapproach . ĠTo Ġthat Ġend , Ġwe Ġextracted Ġall Ġwrong Ġpredictions Ġfrom Ġthe Ġtest Ġset Ġand Ġcomputed Ġthe Ġratio Ġof Ġmis class ifications Ġthat Ġhave Ġnon - English Ġwords Ġin Ġthem . ĠWe Ġfirst Ġextracted Ġall Ġmis class ifications Ġfor Ġa Ġgiven Ġlanguage Ġand Ġfor Ġeach Ġobservation Ġin Ġthe Ġmis class ification Ġset , Ġwe Ġiter ated Ġthrough Ġeach Ġtoken Ġto Ġcheck Ġif Ġthe Ġtoken Ġis Ġin ĠEnglish . ĠIn Ġthis Ġway , Ġwe Ġcounted Ġthe Ġnumber Ġof Ġobservations Ġthat Ġcontained Ġat Ġleast Ġone Ġnon - English Ġword Ġand Ġdivided Ġthat Ġwith Ġthe Ġsize Ġof Ġthe Ġmis class ifications Ġset . ĠWe Ġused Ġthis Ġratio Ġto Ġinvestigate Ġthe Ġeffect Ġof Ġmachine Ġtranslation Ġerrors . </s> We Ġfound Ġthat Ġ25 . 84 % Ġof ĠDutch , Ġ21 . 76 % Ġof ĠTurkish , Ġ24 . 46 % ĠSpanish , Ġand Ġ10 . 71 % Ġof ĠRussian Ġreviews Ġthat Ġwere Ġmis classified Ġhad Ġnon - English Ġwords Ġin Ġthem . ĠThese Ġnon - English Ġwords Ġmight Ġbe Ġcausing Ġthe Ġmis class ifications . ĠHowever , Ġa Ġlarge Ġportion Ġof Ġthe Ġmiss class ifications Ġis Ġnot Ġcaused Ġdue Ġto Ġnot - trans lated Ġwords . ĠAt Ġthe Ġend , Ġthe Ġmachine Ġtranslation Ġerrors Ġhas Ġsome Ġbut Ġnot Ġnoticeable Ġeffects Ġon Ġour Ġmodel . ĠTherefore , Ġwe Ġcan Ġclaim Ġthat Ġmachine Ġtranslation Ġpreserves Ġmost Ġof Ġthe Ġinformation Ġnecessary Ġfor Ġsentiment Ġanalysis . </s> We Ġalso Ġevaluated Ġour Ġmodel Ġwith Ġan ĠEnglish Ġcorpus ĠB IB REF 27 Ġto Ġsee Ġits Ġperformance Ġwithout Ġany Ġinterference Ġfrom Ġmachine Ġtranslation Ġerrors . ĠUsing Ġthe ĠEnglish Ġdata Ġfor Ġtesting , Ġthe Ġmodel Ġachieved Ġ87 . 06 % Ġaccuracy Ġwhere Ġa Ġmajority Ġbaseline Ġwas Ġ68 . 37 % Ġand Ġthe Ġlex icon - based Ġbaseline Ġwas Ġ60 . 10 %. </s> Considering Ġthe Ġimprovements Ġover Ġthe Ġmajority Ġbaseline Ġachieved Ġby Ġthe ĠR NN Ġmodel Ġfor Ġboth Ġnon - English Ġ( on Ġthe Ġaverage Ġ22 . 76 % Ġrelative Ġimprovement ; Ġ15 . 82 % Ġrelative Ġimprovement Ġon ĠSpanish , Ġ72 . 71 % Ġvs . Ġ84 . 21 %, Ġ30 . 53 % Ġrelative Ġimprovement Ġon ĠTurkish , Ġ56 . 97 % Ġvs . Ġ74 . 36 %, Ġ37 . 13 % Ġrelative Ġimprovement Ġon ĠDutch , Ġ59 . 63 % Ġvs . Ġ81 . 77 %, Ġand Ġ7 . 55 % Ġrelative Ġimprovement Ġon ĠRussian , Ġ79 . 60 % Ġvs . Ġ85 . 62 %) Ġand ĠEnglish Ġtest Ġsets Ġ( 27 . 34 % Ġrelative Ġimprovement ), Ġwe Ġcan Ġdraw Ġthe Ġconclusion Ġthat Ġour Ġmodel Ġis Ġrobust Ġto Ġhandle Ġmultiple Ġlanguages . ĠBuilding Ġseparate Ġmodels Ġfor Ġeach Ġlanguage Ġrequires Ġboth Ġlabeled Ġand Ġunl abel ed Ġdata . ĠEven Ġthough Ġhaving Ġlots Ġof Ġlabeled Ġdata Ġin Ġevery Ġlanguage Ġis Ġthe Ġperfect Ġcase , Ġit Ġis Ġunrealistic . ĠTherefore , Ġeliminating Ġthe Ġresource Ġrequirement Ġin Ġthis Ġresource - con str ained Ġtask Ġis Ġcrucial . ĠThe Ġfact Ġthat Ġmachine Ġtranslation Ġcan Ġbe Ġused Ġin Ġre using Ġmodels Ġfrom Ġdifferent Ġlanguages Ġis Ġpromising Ġfor Ġreducing Ġthe Ġdata Ġrequirements . </s> Conclusion </s> Building Ġeffective Ġmachine Ġlearning Ġmodels Ġfor Ġtext Ġrequires Ġdata Ġand Ġdifferent Ġresources Ġsuch Ġas Ġpre - trained Ġword Ġembed d ings Ġand Ġreusable Ġlex icons . ĠUnfortunately , Ġmost Ġof Ġthese Ġresources Ġare Ġnot Ġentirely Ġtransfer able Ġto Ġdifferent Ġdomains , Ġtasks Ġor Ġlanguages . ĠSent iment Ġanalysis Ġis Ġone Ġsuch Ġtask Ġthat Ġrequires Ġadditional Ġeffort Ġto Ġtransfer Ġknowledge Ġbetween Ġlanguages . </s> In Ġthis Ġpaper , Ġwe Ġstudied Ġthe Ġresearch Ġquestion : ĠCan Ġwe Ġbuild Ġreusable Ġsentiment Ġanalysis Ġmodels Ġthat Ġcan Ġbe Ġutilized Ġfor Ġmaking Ġinf erences Ġin Ġdifferent Ġlanguages Ġwithout Ġrequiring Ġseparate Ġmodels Ġand Ġresources Ġfor Ġeach Ġlanguage ? ĠTo Ġthat Ġend , Ġwe Ġbuilt Ġa Ġrecurrent Ġneural Ġnetwork Ġmodel Ġin Ġthe Ġlanguage Ġthat Ġhad Ġlargest Ġdata Ġavailable . ĠWe Ġtook Ġa Ġgeneral - to - specific Ġmodel Ġbuilding Ġstrategy Ġwhere Ġthe Ġlarger Ġcorpus Ġthat Ġhad Ġreviews Ġfrom Ġdifferent Ġdomains Ġwas Ġfirst Ġused Ġto Ġtrain Ġthe ĠR NN Ġmodel Ġand Ġa Ġsmaller Ġsingle - domain Ġcorpus Ġof Ġsentiment Ġreviews Ġwas Ġused Ġto Ġspecialize Ġthe Ġmodel Ġon Ġthe Ġgiven Ġdomain . ĠDuring Ġscoring Ġtime , Ġwe Ġused Ġcorpor a Ġfor Ġthe Ġgiven Ġdomain Ġin Ġdifferent Ġlanguages Ġand Ġtranslated Ġthem Ġto ĠEnglish Ġto Ġbe Ġable Ġto Ġclassify Ġsentiments Ġwith Ġthe Ġtrained Ġmodel . ĠExperimental Ġresults Ġshowed Ġthat Ġthe Ġproposed Ġmult ilingual Ġapproach Ġoutper forms Ġboth Ġthe Ġmajority Ġbaseline Ġand Ġthe Ġlex icon - based Ġbaseline . </s> In Ġthis Ġpaper Ġwe Ġmade Ġthe Ġsentiment Ġanalysis Ġmodel Ġspecific Ġto Ġa Ġsingle Ġdomain . ĠFor Ġfuture Ġwork , Ġwe Ġwould Ġlike Ġto Ġinvestigate Ġthe Ġeffectiveness Ġof Ġour Ġmodel Ġon Ġdifferent Ġreview Ġdomains Ġincluding Ġhotel Ġreviews Ġand Ġon Ġdifferent Ġproblems Ġsuch Ġas Ġdetecting Ġstance .