BIBREF9 , BIBREF8 
EXTRACTIVE
BIBREF9 , BIBREF8
EXTRACTIVE
To test the generalization capability of the proposed approach, we perform training on Dataset 1 and test on Dataset 3. The F1-score drops down dramatically to 33.05%. In order to understand this finding, we visualize each dataset using PCA (Figure FIGREF17 ). It depicts that, although Dataset 1 is mostly linearly separable, Dataset 3 is not. A linear kernel that performs well on Dataset 1 fails to provide good performance on Dataset 3. If we use RBF kernel, it overfits the data and produces worse results than what we get using linear kernel. Similar trends are seen in the performance of other two state-of-the-art approaches BIBREF9 , BIBREF8 . Thus, we decide to perform training on Dataset 3 and test on the Dataset 1. As expected better performance is obtained with F1-score 76.78%. However, the other two state-of-the-art approaches fail to perform well in this setting. While the method by BIBREF9 obtains F1-score of 47.32%, the approach by BIBREF8 achieves 53.02% F1-score when trained on Dataset 3 and tested on Dataset 1. Below, we discuss about this generalizability issue of the models developed or referred in this paper.
As shown in Table TABREF29 , for every feature CNN-SVM outperforms the performance of the CNN. Following BIBREF6 , we have carried out a 5-fold cross-validation on this dataset. The baseline features ( SECREF16 ) perform best among other features. Among all the pre-trained models, the sentiment model (F1-score: 87.00%) achieves better performance in comparison with the other two pre-trained models. Interestingly, when we merge the baseline features with the features extracted by the pre-trained deep NLP models, we only get 0.11% improvement over the F-score. It means that the baseline features alone are quite capable to detect sarcasm. On the other hand, when we combine sentiment, emotion and personality features, we obtain 90.70% F1-score. This indicates that the pre-trained features are indeed useful for sarcasm detection. We also compare our approach with the best research study conducted on this dataset (Table TABREF30 ). Both the proposed baseline model and the baseline + sentiment + emotion + personality model outperform the state of the art BIBREF9 , BIBREF8 . One important difference with the state of the art is that BIBREF8 used relatively larger feature vector size ( INLINEFORM0 500,000) than we used in our experiment (1,100). This not only prevents our model to overfit the data but also speeds up the computation. Thus, we obtain an improvement in the overall performance with automatic feature extraction using a relatively lower dimensional feature space.
1610.08815
Introduction </s> S arc asm Ġis Ġdefined Ġas ĠâĢ ľ a Ġsharp , Ġbitter , Ġor Ġcutting Ġexpression Ġor Ġremark ; Ġa Ġbitter Ġg ibe Ġor Ġtaunt âĢ Ŀ . ĠAs Ġthe Ġfields Ġof Ġaffect ive Ġcomputing Ġand Ġsentiment Ġanalysis Ġhave Ġgained Ġincreasing Ġpopularity ĠB IB REF 0 Ġ, Ġit Ġis Ġa Ġmajor Ġconcern Ġto Ġdetect Ġsarcastic , Ġironic , Ġand Ġmetaph oric Ġexpressions . ĠS arc asm , Ġespecially , Ġis Ġkey Ġfor Ġsentiment Ġanalysis Ġas Ġit Ġcan Ġcompletely Ġflip Ġthe Ġpol arity Ġof Ġopinions . ĠUnderstanding Ġthe Ġground Ġtruth , Ġor Ġthe Ġfacts Ġabout Ġa Ġgiven Ġevent , Ġallows Ġfor Ġthe Ġdetection Ġof Ġcontradiction Ġbetween Ġthe Ġobjective Ġpol arity Ġof Ġthe Ġevent Ġ( usually Ġnegative ) Ġand Ġits Ġsarcastic Ġcharacteristic Ġby Ġthe Ġauthor Ġ( usually Ġpositive ), Ġas Ġin ĠâĢ ľ I Ġlove Ġthe Ġpain Ġof Ġbreakup âĢ Ŀ . ĠOb taining Ġsuch Ġknowledge Ġis , Ġhowever , Ġvery Ġdifficult . </s> In Ġour Ġexperiments , Ġwe Ġexposed Ġthe Ġclass ifier Ġto Ġsuch Ġknowledge Ġextracted Ġindirectly Ġfrom ĠTwitter . ĠName ly , Ġwe Ġused ĠTwitter Ġdata Ġcrawled Ġin Ġa Ġtime Ġperiod , Ġwhich Ġlikely Ġcontain Ġboth Ġthe Ġsarcastic Ġand Ġnon - s ar cast ic Ġaccounts Ġof Ġan Ġevent Ġor Ġsimilar Ġevents . ĠWe Ġbelieve Ġthat Ġunamb iguous Ġnon - s ar cast ic Ġsentences Ġprovided Ġthe Ġclass ifier Ġwith Ġthe Ġground - truth Ġpol arity Ġof Ġthose Ġevents , Ġwhich Ġthe Ġclass ifier Ġcould Ġthen Ġcontrast Ġwith Ġthe Ġopposite Ġestim ations Ġin Ġsarcastic Ġsentences . ĠTwitter Ġis Ġa Ġmore Ġsuitable Ġresource Ġfor Ġthis Ġpurpose Ġthan Ġblog Ġposts , Ġbecause Ġthe Ġpol arity Ġof Ġshort Ġtweets Ġis Ġeasier Ġto Ġdetect Ġ( as Ġall Ġthe Ġinformation Ġnecessary Ġto Ġdetect Ġpol arity Ġis Ġlikely Ġto Ġbe Ġcontained Ġin Ġthe Ġsame Ġsentence ) Ġand Ġbecause Ġthe ĠTwitter ĠAPI Ġmakes Ġit Ġeasy Ġto Ġcollect Ġa Ġlarge Ġcorpus Ġof Ġtweets Ġcontaining Ġboth Ġsarcastic Ġand Ġnon - s ar cast ic Ġexamples Ġof Ġthe Ġsame Ġevent . </s> Sometimes , Ġhowever , Ġjust Ġknowing Ġthe Ġground Ġtruth Ġor Ġsimple Ġfacts Ġon Ġthe Ġtopic Ġis Ġnot Ġenough , Ġas Ġthe Ġtext Ġmay Ġrefer Ġto Ġother Ġevents Ġin Ġorder Ġto Ġexpress Ġsarc asm . ĠFor Ġexample , Ġthe Ġsentence ĠâĢ ľ If ĠHillary Ġwins , Ġshe Ġwill Ġsurely Ġbe Ġpleased Ġto Ġrecall ĠMonica Ġeach Ġtime Ġshe Ġenters Ġthe ĠOval ĠOffice Ġ: P Ġ: D âĢ Ŀ , Ġwhich Ġrefers Ġto Ġthe Ġ2016 ĠUS Ġpresidential Ġelection Ġcampaign Ġand Ġto Ġthe Ġevents Ġof Ġearly Ġ1990 's Ġrelated Ġto Ġthe ĠUS Ġpresident ĠClinton , Ġis Ġsarcastic Ġbecause ĠHillary , Ġa Ġcandidate Ġand ĠClinton 's Ġwife , Ġwould Ġin Ġfact Ġnot Ġbe Ġpleased Ġto Ġrecall Ġher Ġhusband 's Ġalleged Ġpast Ġaffair Ġwith ĠMonica ĠLew insky . ĠThe Ġsystem , Ġhowever , Ġwould Ġneed Ġa Ġconsiderable Ġamount Ġof Ġfacts , Ġcommons ense Ġknowledge , Ġan aph ora Ġresolution , Ġand Ġlogical Ġreasoning Ġto Ġdraw Ġsuch Ġa Ġconclusion . ĠIn Ġthis Ġpaper , Ġwe Ġwill Ġnot Ġdeal Ġwith Ġsuch Ġcomplex Ġcases . </s> Ex isting Ġworks Ġon Ġsarc asm Ġdetection Ġhave Ġmainly Ġfocused Ġon Ġun ig rams Ġand Ġthe Ġuse Ġof Ġemot icons ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ, Ġun super vised Ġpattern Ġmining Ġapproach ĠB IB REF 4 Ġ, Ġsemi - super vised Ġapproach ĠB IB REF 5 Ġand Ġn - gram s Ġbased Ġapproach ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġwith Ġsentiment Ġfeatures . ĠInstead , Ġwe Ġpropose Ġa Ġframework Ġthat Ġlearns Ġsarc asm Ġfeatures Ġautomatically Ġfrom Ġa Ġsarc asm Ġcorpus Ġusing Ġa Ġconv olution al Ġneural Ġnetwork Ġ( CNN ). ĠWe Ġalso Ġinvestigate Ġwhether Ġfeatures Ġextracted Ġusing Ġthe Ġpre - trained Ġsentiment , Ġemotion Ġand Ġpersonality Ġmodels Ġcan Ġimprove Ġsarc asm Ġdetection Ġperformance . ĠOur Ġapproach Ġuses Ġrelatively Ġlower Ġdimensional Ġfeature Ġvectors Ġand Ġoutper forms Ġthe Ġstate Ġof Ġthe Ġart Ġon Ġdifferent Ġdatasets . ĠIn Ġsummary , Ġthe Ġmain Ġcontributions Ġof Ġthis Ġpaper Ġare Ġthe Ġfollowing : </s> The Ġrest Ġof Ġthe Ġpaper Ġis Ġorganized Ġas Ġfollows : ĠSection ĠSEC REF 2 Ġproposes Ġa Ġbrief Ġliterature Ġreview Ġon Ġsarc asm Ġdetection ; ĠSection ĠSEC REF 4 Ġpresents Ġthe Ġproposed Ġapproach ; Ġexperimental Ġresults Ġand Ġthorough Ġdiscussion Ġon Ġthe Ġexperiments Ġare Ġgiven Ġin ĠSection ĠSEC REF 5 Ġ; Ġfinally , ĠSection ĠSEC REF 6 Ġconcludes Ġthe Ġpaper . </s> Related ĠWorks </s> N LP Ġresearch Ġis Ġgradually Ġevolving Ġfrom Ġlex ical Ġto Ġcompos itional Ġsemantics ĠB IB REF 10 Ġthrough Ġthe Ġadoption Ġof Ġnovel Ġmeaning - pres erving Ġand Ġcontext - aware Ġparad ig ms Ġsuch Ġas Ġconv olution al Ġnetworks ĠB IB REF 11 Ġ, Ġrecurrent Ġbelief Ġnetworks ĠB IB REF 12 Ġ, Ġstatistical Ġlearning Ġtheory ĠB IB REF 13 Ġ, Ġconv olution al Ġmultiple Ġkernel Ġlearning ĠB IB REF 14 Ġ, Ġand Ġcommons ense Ġreasoning ĠB IB REF 15 Ġ. ĠBut Ġwhile Ġother ĠN LP Ġtasks Ġhave Ġbeen Ġextensively Ġinvestigated , Ġsarc asm Ġdetection Ġis Ġa Ġrelatively Ġnew Ġresearch Ġtopic Ġwhich Ġhas Ġgained Ġincreasing Ġinterest Ġonly Ġrecently , Ġpartly Ġthanks Ġto Ġthe Ġrise Ġof Ġsocial Ġmedia Ġanalytics Ġand Ġsentiment Ġanalysis . ĠSent iment Ġanalysis ĠB IB REF 16 Ġand Ġusing Ġmultim od al Ġinformation Ġas Ġa Ġnew Ġtrend ĠB IB REF 17 Ġ, ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ, ĠB IB REF 14 Ġis Ġa Ġpopular Ġbranch Ġof ĠN LP Ġresearch Ġthat Ġaims Ġto Ġunderstand Ġsentiment Ġof Ġdocuments Ġautomatically Ġusing Ġcombination Ġof Ġvarious Ġmachine Ġlearning Ġapproaches ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ, ĠB IB REF 20 Ġ, ĠB IB REF 23 Ġ. </s> An Ġearly Ġwork Ġin Ġthis Ġfield Ġwas Ġdone Ġby ĠB IB REF 6 Ġon Ġa Ġdataset Ġof Ġ6 , 600 Ġmanually Ġannot ated ĠAmazon Ġreviews Ġusing Ġa Ġk NN - class ifier Ġover Ġpunct uation - based Ġand Ġpattern - based Ġfeatures , Ġi . e ., Ġordered Ġsequence Ġof Ġhigh Ġfrequency Ġwords . ĠB IB REF 1 Ġused Ġsupport Ġvector Ġmachine Ġ( S VM ) Ġand Ġlog istic Ġregression Ġover Ġa Ġfeature Ġset Ġof Ġun ig rams , Ġdictionary - based Ġlex ical Ġfeatures Ġand Ġpragmatic Ġfeatures Ġ( e . g ., Ġemot icons ) Ġand Ġcompared Ġthe Ġperformance Ġof Ġthe Ġclass ifier Ġwith Ġthat Ġof Ġhumans . ĠB IB REF 24 Ġdescribed Ġa Ġset Ġof Ġtextual Ġfeatures Ġfor Ġrecognizing Ġirony Ġat Ġa Ġlinguistic Ġlevel , Ġespecially Ġin Ġshort Ġtexts Ġcreated Ġvia ĠTwitter , Ġand Ġconstructed Ġa Ġnew Ġmodel Ġthat Ġwas Ġassessed Ġalong Ġtwo Ġdimensions : Ġrepresent at iveness Ġand Ġrelevance . ĠB IB REF 5 Ġused Ġthe Ġpresence Ġof Ġa Ġpositive Ġsentiment Ġin Ġclose Ġproximity Ġof Ġa Ġnegative Ġsituation Ġphrase Ġas Ġa Ġfeature Ġfor Ġsarc asm Ġdetection . ĠB IB REF 25 Ġused Ġthe ĠBalanced ĠWindow Ġalgorithm Ġfor Ġclass ifying ĠDutch Ġtweets Ġas Ġsarcastic Ġvs . Ġnon - s ar cast ic ; Ġn - gram s Ġ( uni , Ġbi Ġand Ġtri ) Ġand Ġintens ifiers Ġwere Ġused Ġas Ġfeatures Ġfor Ġclassification . </s> BI B REF 26 Ġcompared Ġthe Ġperformance Ġof Ġdifferent Ġclass ifiers Ġon Ġthe ĠAmazon Ġreview Ġdataset Ġusing Ġthe Ġimbalance Ġbetween Ġthe Ġsentiment Ġexpressed Ġby Ġthe Ġreview Ġand Ġthe Ġuser - given Ġstar Ġrating . ĠFeatures Ġbased Ġon Ġfrequency Ġ( gap Ġbetween Ġrare Ġand Ġcommon Ġwords ), Ġwritten Ġspoken Ġgap Ġ( in Ġterms Ġof Ġdifference Ġbetween Ġusage ), Ġsyn onyms Ġ( based Ġon Ġthe Ġdifference Ġin Ġfrequency Ġof Ġsyn onyms ) Ġand Ġambiguity Ġ( number Ġof Ġwords Ġwith Ġmany Ġsyn onyms ) Ġwere Ġused Ġby ĠB IB REF 3 Ġfor Ġsarc asm Ġdetection Ġin Ġtweets . ĠB IB REF 9 Ġproposed Ġthe Ġuse Ġof Ġimplicit Ġinc ong ru ity Ġand Ġexplicit Ġinc ong ru ity Ġbased Ġfeatures Ġalong Ġwith Ġlex ical Ġand Ġpragmatic Ġfeatures , Ġsuch Ġas Ġemot icons Ġand Ġpunct uation Ġmarks . ĠTheir Ġmethod Ġis Ġvery Ġmuch Ġsimilar Ġto Ġthe Ġmethod Ġproposed Ġby ĠB IB REF 5 Ġexcept ĠB IB REF 9 Ġused Ġexplicit Ġinc ong ru ity Ġfeatures . ĠTheir Ġmethod Ġoutper forms Ġthe Ġapproach Ġby ĠB IB REF 5 Ġon Ġtwo Ġdatasets . </s> BI B REF 8 Ġcompared Ġthe Ġperformance Ġwith Ġdifferent Ġlanguage - independent Ġfeatures Ġand Ġpre - processing Ġtechniques Ġfor Ġclass ifying Ġtext Ġas Ġsarcastic Ġand Ġnon - s ar cast ic . ĠThe Ġcomparison Ġwas Ġdone Ġover Ġthree ĠTwitter Ġdataset Ġin Ġtwo Ġdifferent Ġlanguages , Ġtwo Ġof Ġthese Ġin ĠEnglish Ġwith Ġa Ġbalanced Ġand Ġan Ġim balanced Ġdistribution Ġand Ġthe Ġthird Ġone Ġin ĠCzech . ĠThe Ġfeature Ġset Ġincluded Ġn - gram s , Ġword - shape Ġpatterns , Ġpointed ness Ġand Ġpunct uation - based Ġfeatures . </s> In Ġthis Ġwork , Ġwe Ġuse Ġfeatures Ġextracted Ġfrom Ġa Ġdeep ĠCNN Ġfor Ġsarc asm Ġdetection . ĠSome Ġof Ġthe Ġkey Ġdifferences Ġbetween Ġthe Ġproposed Ġapproach Ġand Ġexisting Ġmethods Ġinclude Ġthe Ġuse Ġof Ġa Ġrelatively Ġsmaller Ġfeature Ġset , Ġautomatic Ġfeature Ġextraction , Ġthe Ġuse Ġof Ġdeep Ġnetworks , Ġand Ġthe Ġadoption Ġof Ġpre - trained ĠN LP Ġmodels . </s> Sent iment ĠAnalysis Ġand ĠS arc asm ĠDetection </s> S arc asm Ġdetection Ġis Ġan Ġimportant Ġsubt ask Ġof Ġsentiment Ġanalysis ĠB IB REF 27 Ġ. ĠSince Ġsarcastic Ġsentences Ġare Ġsubjective , Ġthey Ġcarry Ġsentiment Ġand Ġemotion - bearing Ġinformation . ĠMost Ġof Ġthe Ġstudies Ġin Ġthe Ġliterature ĠB IB REF 28 Ġ, ĠB IB REF 29 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 30 Ġinclude Ġsentiment Ġfeatures Ġin Ġsarc asm Ġdetection Ġwith Ġthe Ġuse Ġof Ġa Ġstate - of - the - art Ġsentiment Ġlex icon . ĠBelow , Ġwe Ġexplain Ġhow Ġsentiment Ġinformation Ġis Ġkey Ġto Ġexpress Ġsarcastic Ġopinions Ġand Ġthe Ġapproach Ġwe Ġundertake Ġto Ġexploit Ġsuch Ġinformation Ġfor Ġsarc asm Ġdetection . </s> In Ġgeneral , Ġmost Ġsarcastic Ġsentences Ġcontradict Ġthe Ġfact . ĠIn Ġthe Ġsentence ĠâĢ ľ I Ġlove Ġthe Ġpain Ġpresent Ġin Ġthe Ġbreak ups " Ġ( Figure ĠFIG REF 4 Ġ), Ġfor Ġexample , Ġthe Ġword ĠâĢ ľ love " Ġcontradicts ĠâĢ ľ pain Ġpresent Ġin Ġthe Ġbreak ups âĢ Ŀ , Ġbecause Ġin Ġgeneral Ġno - one Ġloves Ġto Ġbe Ġin Ġpain . ĠIn Ġthis Ġcase , Ġthe Ġfact Ġ( i . e ., ĠâĢ ľ pain Ġin Ġthe Ġbreak ups ") Ġand Ġthe Ġcontradictory Ġstatement Ġto Ġthat Ġfact Ġ( i . e ., ĠâĢ ľ I Ġlove ") Ġexpress Ġsentiment Ġexplicitly . ĠSent iment Ġshifts Ġfrom Ġpositive Ġto Ġnegative Ġbut , Ġaccording Ġto Ġsent ic Ġpatterns ĠB IB REF 31 Ġ, Ġthe Ġliteral Ġsentiment Ġremains Ġpositive . ĠSent ic Ġpatterns , Ġin Ġfact , Ġaim Ġto Ġdetect Ġthe Ġpol arity Ġexpressed Ġby Ġthe Ġspeaker ; Ġthus , Ġwhenever Ġthe Ġconstruction ĠâĢ ľ I Ġlove âĢ Ŀ Ġis Ġencountered , Ġthe Ġsentence Ġis Ġpositive Ġno Ġmatter Ġwhat Ġcomes Ġafter Ġit Ġ( e . g ., ĠâĢ ľ I Ġlove Ġthe Ġmovie Ġthat Ġyou Ġhate âĢ Ŀ ). ĠIn Ġthis Ġcase , Ġhowever , Ġthe Ġsentence Ġcarries Ġsarc asm Ġand , Ġhence , Ġreflects Ġthe Ġnegative Ġsentiment Ġof Ġthe Ġspeaker . </s> In Ġanother Ġexample Ġ( Figure ĠFIG REF 4 Ġ), Ġthe Ġfact , Ġi . e ., ĠâĢ ľ I Ġleft Ġthe Ġtheater Ġduring Ġthe Ġinterval ", Ġhas Ġimplicit Ġnegative Ġsentiment . ĠThe Ġstatement ĠâĢ ľ I Ġlove Ġthe Ġmovie " Ġcontradicts Ġthe Ġfact ĠâĢ ľ I Ġleft Ġthe Ġtheater Ġduring Ġthe Ġinterval "; Ġthus , Ġthe Ġsentence Ġis Ġsarcastic . ĠAlso Ġin Ġthis Ġcase Ġthe Ġsentiment Ġshifts Ġfrom Ġpositive Ġto Ġnegative Ġand Ġhints Ġat Ġthe Ġsarcastic Ġnature Ġof Ġthe Ġopinion . </s> The Ġabove Ġdiscussion Ġhas Ġmade Ġclear Ġthat Ġsentiment Ġ( and , Ġin Ġparticular , Ġsentiment Ġshifts ) Ġcan Ġlargely Ġhelp Ġto Ġdetect Ġsarc asm . ĠIn Ġorder Ġto Ġinclude Ġsentiment Ġshifting Ġinto Ġthe Ġproposed Ġframework , Ġwe Ġtrain Ġa Ġsentiment Ġmodel Ġfor Ġsentiment - specific Ġfeature Ġextraction . ĠTraining Ġwith Ġa ĠCNN Ġhelps Ġto Ġcombine Ġthe Ġlocal Ġfeatures Ġin Ġthe Ġlower Ġlayers Ġinto Ġglobal Ġfeatures Ġin Ġthe Ġhigher Ġlayers . ĠWe Ġdo Ġnot Ġmake Ġuse Ġof Ġsent ic Ġpatterns ĠB IB REF 31 Ġin Ġthis Ġpaper Ġbut Ġwe Ġdo Ġplan Ġto Ġexplore Ġthat Ġresearch Ġdirection Ġas Ġa Ġpart Ġof Ġour Ġfuture Ġwork . ĠIn Ġthe Ġliterature , Ġit Ġis Ġfound Ġthat Ġsarc asm Ġis Ġuser - specific Ġtoo , Ġi . e ., Ġsome Ġusers Ġhave Ġa Ġparticular Ġtendency Ġto Ġpost Ġmore Ġsarcastic Ġtweets Ġthan Ġothers . ĠThis Ġacts Ġas Ġa Ġprimary Ġintuition Ġfor Ġus Ġto Ġextract Ġpersonality - based Ġfeatures Ġfor Ġsarc asm Ġdetection . </s> The ĠProp osed ĠFramework </s> As Ġdiscussed Ġin Ġthe Ġliterature ĠB IB REF 5 Ġ, Ġsarc asm Ġdetection Ġmay Ġdepend Ġon Ġsentiment Ġand Ġother Ġcognitive Ġaspects . ĠFor Ġthis Ġreason , Ġwe Ġincorporate Ġboth Ġsentiment Ġand Ġemotion Ġclues Ġin Ġour Ġframework . ĠAlong Ġwith Ġthese , Ġwe Ġalso Ġargue Ġthat Ġpersonality Ġof Ġthe Ġopinion Ġholder Ġis Ġan Ġimportant Ġfactor Ġfor Ġsarc asm Ġdetection . ĠIn Ġorder Ġto Ġaddress Ġall Ġof Ġthese Ġvariables , Ġwe Ġcreate Ġdifferent Ġmodels Ġfor Ġeach Ġof Ġthem , Ġnamely : Ġsentiment , Ġemotion Ġand Ġpersonality . ĠThe Ġidea Ġis Ġto Ġtrain Ġeach Ġmodel Ġon Ġits Ġcorresponding Ġbenchmark Ġdataset Ġand , Ġhence , Ġuse Ġsuch Ġpre - trained Ġmodels Ġtogether Ġto Ġextract Ġsarc asm - related Ġfeatures Ġfrom Ġthe Ġsarc asm Ġdatasets . </s> Now , Ġthe Ġviable Ġresearch Ġquestion Ġhere Ġis Ġ- ĠDo Ġthese Ġmodels Ġhelp Ġto Ġimprove Ġsarc asm Ġdetection Ġperformance ?' ĠLiterature Ġshows Ġthat Ġthey Ġimprove Ġthe Ġperformance Ġbut Ġnot Ġsignificantly . ĠThus , Ġdo Ġwe Ġneed Ġto Ġconsider Ġthose Ġfactors Ġin Ġspotting Ġsarcastic Ġsentences ? ĠAren 't Ġn - gram s Ġenough Ġfor Ġsarc asm Ġdetection ? ĠThroughout Ġthe Ġrest Ġof Ġthis Ġpaper , Ġwe Ġaddress Ġthese Ġquestions Ġin Ġdetail . ĠThe Ġtraining Ġof Ġeach Ġmodel Ġis Ġdone Ġusing Ġa ĠCNN . ĠBelow , Ġwe Ġexplain Ġthe Ġframework Ġin Ġdetail . ĠThen , Ġwe Ġdiscuss Ġthe Ġpre - trained Ġmodels . ĠFigure ĠFIG REF 6 Ġpresents Ġa Ġvisualization Ġof Ġthe Ġproposed Ġframework . </s> General ĠCNN ĠFramework </s> CNN Ġcan Ġautomatically Ġextract Ġkey Ġfeatures Ġfrom Ġthe Ġtraining Ġdata . ĠIt Ġgr as ps Ġcontextual Ġlocal Ġfeatures Ġfrom Ġa Ġsentence Ġand , Ġafter Ġseveral Ġconv olution Ġoperations , Ġit Ġforms Ġa Ġglobal Ġfeature Ġvector Ġout Ġof Ġthose Ġlocal Ġfeatures . ĠCNN Ġdoes Ġnot Ġneed Ġthe Ġhand - crafted Ġfeatures Ġused Ġin Ġtraditional Ġsupervised Ġclass ifiers . ĠSuch Ġhand - crafted Ġfeatures Ġare Ġdifficult Ġto Ġcompute Ġand Ġa Ġgood Ġguess Ġfor Ġencoding Ġthe Ġfeatures Ġis Ġalways Ġnecessary Ġin Ġorder Ġto Ġget Ġsatisfactory Ġresults . ĠCNN , Ġinstead , Ġuses Ġa Ġhierarchy Ġof Ġlocal Ġfeatures Ġwhich Ġare Ġimportant Ġto Ġlearn Ġcontext . ĠThe Ġhand - crafted Ġfeatures Ġoften Ġignore Ġsuch Ġa Ġhierarchy Ġof Ġlocal Ġfeatures . </s> Features Ġextracted Ġby ĠCNN Ġcan Ġtherefore Ġbe Ġused Ġinstead Ġof Ġhand - crafted Ġfeatures , Ġas Ġthey Ġcarry Ġmore Ġuseful Ġinformation . ĠThe Ġidea Ġbehind Ġconv olution Ġis Ġto Ġtake Ġthe Ġdot Ġproduct Ġof Ġa Ġvector Ġof ĠIN LINE FORM 0 Ġweights ĠIN LINE FORM 1 Ġalso Ġknown Ġas Ġkernel Ġvector Ġwith Ġeach ĠIN LINE FORM 2 Ġ- gram Ġin Ġthe Ġsentence ĠIN LINE FORM 3 Ġto Ġobtain Ġanother Ġsequence Ġof Ġfeatures ĠIN LINE FORM 4 Ġ. ĠDIS PLAY FORM 0 </s> Thus , Ġa Ġmax Ġpool ing Ġoperation Ġis Ġapplied Ġover Ġthe Ġfeature Ġmap Ġand Ġthe Ġmaximum Ġvalue ĠIN LINE FORM 0 Ġis Ġtaken Ġas Ġthe Ġfeature Ġcorresponding Ġto Ġthis Ġparticular Ġkernel Ġvector . ĠSimilarly , Ġvarying Ġkernel Ġvectors Ġand Ġwindow Ġsizes Ġare Ġused Ġto Ġobtain Ġmultiple Ġfeatures ĠB IB REF 32 Ġ. ĠFor Ġeach Ġword ĠIN LINE FORM 1 Ġin Ġthe Ġvocabulary , Ġa ĠIN LINE FORM 2 Ġ- dimensional Ġvector Ġrepresentation Ġis Ġgiven Ġin Ġa Ġlook Ġup Ġtable Ġthat Ġis Ġlearned Ġfrom Ġthe Ġdata ĠB IB REF 33 Ġ. ĠThe Ġvector Ġrepresentation Ġof Ġa Ġsentence , Ġhence , Ġis Ġa Ġconc aten ation Ġof Ġvectors Ġfor Ġindividual Ġwords . ĠSimilarly , Ġwe Ġcan Ġhave Ġlook Ġup Ġtables Ġfor Ġother Ġfeatures . ĠOne Ġmight Ġwant Ġto Ġprovide Ġfeatures Ġother Ġthan Ġwords Ġif Ġthese Ġfeatures Ġare Ġsuspected Ġto Ġbe Ġhelpful . ĠThe Ġconv olution Ġkernels Ġare Ġthen Ġapplied Ġto Ġword Ġvectors Ġinstead Ġof Ġindividual Ġwords . </s> We Ġuse Ġthese Ġfeatures Ġto Ġtrain Ġhigher Ġlayers Ġof Ġthe ĠCNN , Ġin Ġorder Ġto Ġrepresent Ġbigger Ġgroups Ġof Ġwords Ġin Ġsentences . ĠWe Ġdenote Ġthe Ġfeature Ġlearned Ġat Ġhidden Ġneuron ĠIN LINE FORM 0 Ġin Ġlayer ĠIN LINE FORM 1 Ġas ĠIN LINE FORM 2 Ġ. ĠMultiple Ġfeatures Ġmay Ġbe Ġlearned Ġin Ġparallel Ġin Ġthe Ġsame ĠCNN Ġlayer . ĠThe Ġfeatures Ġlearned Ġin Ġeach Ġlayer Ġare Ġused Ġto Ġtrain Ġthe Ġnext Ġlayer : ĠDIS PLAY FORM 0 </s> where Ġ* Ġindicates Ġconv olution Ġand ĠIN LINE FORM 0 Ġis Ġa Ġweight Ġkernel Ġfor Ġhidden Ġneuron ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġis Ġthe Ġtotal Ġnumber Ġof Ġhidden Ġneurons . ĠThe ĠCNN Ġsentence Ġmodel Ġpreserves Ġthe Ġorder Ġof Ġwords Ġby Ġadopting Ġconv olution Ġkernels Ġof Ġgradually Ġincreasing Ġsizes Ġthat Ġspan Ġan Ġincreasing Ġnumber Ġof Ġwords Ġand Ġultimately Ġthe Ġentire Ġsentence . ĠAs Ġmentioned Ġabove , Ġeach Ġword Ġin Ġa Ġsentence Ġis Ġrepresented Ġusing Ġword Ġembed d ings . </s> We Ġemploy Ġthe Ġpublicly Ġavailable Ġword 2 vec Ġvectors , Ġwhich Ġwere Ġtrained Ġon Ġ100 Ġbillion Ġwords Ġfrom ĠGoogle ĠNews . ĠThe Ġvectors Ġare Ġof Ġdimension ality Ġ300 , Ġtrained Ġusing Ġthe Ġcontinuous Ġbag - of - words Ġarchitecture ĠB IB REF 33 Ġ. ĠWords Ġnot Ġpresent Ġin Ġthe Ġset Ġof Ġpre - trained Ġwords Ġare Ġinitialized Ġrandomly . ĠHowever , Ġwhile Ġtraining Ġthe Ġneural Ġnetwork , Ġwe Ġuse Ġnon - static Ġrepresentations . ĠThese Ġinclude Ġthe Ġword Ġvectors , Ġtaken Ġas Ġinput , Ġinto Ġthe Ġlist Ġof Ġparameters Ġto Ġbe Ġlearned Ġduring Ġtraining . </s> Two Ġprimary Ġreasons Ġmotivated Ġus Ġto Ġuse Ġnon - static Ġchannels Ġas Ġopposed Ġto Ġstatic Ġones . ĠFirstly , Ġthe Ġcommon Ġpresence Ġof Ġinformal Ġlanguage Ġand Ġwords Ġin Ġtweets Ġresulted Ġin Ġa Ġrelatively Ġhigh Ġrandom Ġinitialization Ġof Ġword Ġvectors Ġdue Ġto Ġthe Ġun availability Ġof Ġthese Ġwords Ġin Ġthe Ġword 2 vec Ġdictionary . ĠSecondly , Ġsarcastic Ġsentences Ġare Ġknown Ġto Ġinclude Ġpol arity Ġshifts Ġin Ġsentimental Ġand Ġemotional Ġdegrees . ĠFor Ġexample , ĠâĢ ľ I Ġlove Ġthe Ġpain Ġpresent Ġin Ġbreak ups " Ġis Ġa Ġsarcastic Ġsentence Ġwith Ġa Ġsignificant Ġchange Ġin Ġsentimental Ġpol arity . ĠAs Ġword 2 vec Ġwas Ġnot Ġtrained Ġto Ġincorporate Ġthese Ġnuances , Ġwe Ġallow Ġour Ġmodels Ġto Ġupdate Ġthe Ġembed d ings Ġduring Ġtraining Ġin Ġorder Ġto Ġinclude Ġthem . ĠEach Ġsentence Ġis Ġwrapped Ġto Ġa Ġwindow Ġof ĠIN LINE FORM 0 Ġ, Ġwhere ĠIN LINE FORM 1 Ġis Ġthe Ġmaximum Ġnumber Ġof Ġwords Ġamongst Ġall Ġsentences Ġin Ġthe Ġdataset . ĠWe Ġuse Ġthe Ġoutput Ġof Ġthe Ġfully - connected Ġlayer Ġof Ġthe Ġnetwork Ġas Ġour Ġfeature Ġvector . </s> We Ġhave Ġdone Ġtwo Ġkinds Ġof Ġexperiments : Ġfirst ly , Ġwe Ġused ĠCNN Ġfor Ġthe Ġclassification ; Ġsecondly , Ġwe Ġextracted Ġfeatures Ġfrom Ġthe Ġfully - connected Ġlayer Ġof Ġthe ĠCNN Ġand Ġfed Ġthem Ġto Ġan ĠS VM Ġfor Ġthe Ġfinal Ġclassification . ĠThe Ġlatter ĠCNN - S VM Ġscheme Ġis Ġquite Ġuseful Ġfor Ġtext Ġclassification Ġas Ġshown Ġby ĠP oria Ġet Ġal . ĠB IB REF 18 Ġ. ĠWe Ġcarry Ġout Ġn - fold Ġcross - valid ation Ġon Ġthe Ġdataset Ġusing ĠCNN . ĠIn Ġevery Ġfold Ġiteration , Ġin Ġorder Ġto Ġobtain Ġthe Ġtraining Ġand Ġtest Ġfeatures , Ġthe Ġoutput Ġof Ġthe Ġfully - connected Ġlayer Ġis Ġtreated Ġas Ġfeatures Ġto Ġbe Ġused Ġfor Ġthe Ġfinal Ġclassification Ġusing ĠS VM . ĠTable ĠT AB REF 12 Ġshows Ġthe Ġtraining Ġsettings Ġfor Ġeach ĠCNN Ġmodel Ġdeveloped Ġin Ġthis Ġwork . ĠRe LU Ġis Ġused Ġas Ġthe Ġnon - linear Ġactivation Ġfunction Ġof Ġthe Ġnetwork . ĠThe Ġnetwork Ġconfigurations Ġof Ġall Ġmodels Ġdeveloped Ġin Ġthis Ġwork Ġare Ġgiven Ġin ĠTable ĠT AB REF 12 Ġ. </s> Sent iment ĠFeature ĠExt raction ĠModel </s> As Ġdiscussed Ġabove , Ġsentiment Ġclues Ġplay Ġan Ġimportant Ġrole Ġfor Ġsarcastic Ġsentence Ġdetection . ĠIn Ġour Ġwork , Ġwe Ġtrain Ġa ĠCNN Ġ( see ĠSection ĠSEC REF 5 Ġfor Ġdetails ) Ġon Ġa Ġsentiment Ġbenchmark Ġdataset . ĠThis Ġpre - trained Ġmodel Ġis Ġthen Ġused Ġto Ġextract Ġfeatures Ġfrom Ġthe Ġsarcastic Ġdatasets . ĠIn Ġparticular , Ġwe Ġuse ĠSem eval Ġ2014 ĠB IB REF 34 ĠTwitter ĠSent iment ĠAnalysis ĠDat as et Ġfor Ġthe Ġtraining . ĠThis Ġdataset Ġcontains Ġ9 , 497 Ġtweets Ġout Ġof Ġwhich Ġ5 , 8 95 Ġare Ġpositive , Ġ3 , 131 Ġare Ġnegative Ġand Ġ4 71 Ġare Ġneutral . ĠThe Ġfully - connected Ġlayer Ġof Ġthe ĠCNN Ġused Ġfor Ġsentiment Ġfeature Ġextraction Ġhas Ġ100 Ġneurons , Ġso Ġ100 Ġfeatures Ġare Ġextracted Ġfrom Ġthis Ġpre - trained Ġmodel . ĠThe Ġfinal Ġsoft max Ġdetermines Ġwhether Ġa Ġsentence Ġis Ġpositive , Ġnegative Ġor Ġneutral . ĠThus , Ġwe Ġhave Ġthree Ġneurons Ġin Ġthe Ġsoft max Ġlayer . </s> Em otion ĠFeature ĠExt raction ĠModel </s> We Ġuse Ġthe ĠCNN Ġstructure Ġas Ġdescribed Ġin ĠSection ĠSEC REF 5 Ġfor Ġemotional Ġfeature Ġextraction . ĠAs Ġa Ġdataset Ġfor Ġextracting Ġemotion - related Ġfeatures , Ġwe Ġuse Ġthe Ġcorpus Ġdeveloped Ġby ĠB IB REF 35 Ġ. ĠThis Ġdataset Ġconsists Ġof Ġblog Ġposts Ġlabeled Ġby Ġtheir Ġcorresponding Ġemotion Ġcategories . ĠAs Ġemotion Ġtax onomy , Ġthe Ġauthors Ġused Ġsix Ġbasic Ġemotions , Ġi . e ., ĠAnger , ĠDis g ust , ĠSurprise , ĠSad ness , ĠJoy Ġand ĠFear . ĠIn Ġparticular , Ġthe Ġblog Ġposts Ġwere Ġsplit Ġinto Ġsentences Ġand Ġeach Ġsentence Ġwas Ġlabeled . ĠThe Ġdataset Ġcontains Ġ5 , 205 Ġsentences Ġlabeled Ġby Ġone Ġof Ġthe Ġemotion Ġlabels . ĠAfter Ġemploying Ġthis Ġmodel Ġon Ġthe Ġsarc asm Ġdataset , Ġwe Ġobtained Ġa Ġ150 - dimensional Ġfeature Ġvector Ġfrom Ġthe Ġfully - connected Ġlayer . ĠAs Ġthe Ġaim Ġof Ġtraining Ġis Ġto Ġclassify Ġeach Ġsentence Ġinto Ġone Ġof Ġthe Ġsix Ġemotion Ġclasses , Ġwe Ġused Ġsix Ġneurons Ġin Ġthe Ġsoft max Ġlayer . </s> Person ality ĠFeature ĠExt raction ĠModel </s> Detect ing Ġpersonality Ġfrom Ġtext Ġis Ġa Ġwell - known Ġchallenging Ġproblem . ĠIn Ġour Ġwork , Ġwe Ġuse Ġfive Ġpersonality Ġtraits Ġdescribed Ġby ĠB IB REF 36 Ġ, Ġi . e ., ĠOpen ness , ĠCons cientious ness , ĠExtra version , ĠAg ree abl eness , Ġand ĠNeuro t icism , Ġsometimes Ġabbrevi ated Ġas ĠO CE AN Ġ( by Ġtheir Ġfirst Ġletters ). ĠAs Ġa Ġtraining Ġdataset , Ġwe Ġuse Ġthe Ġcorpus Ġdeveloped Ġby ĠB IB REF 36 Ġ, Ġwhich Ġcontains Ġ2 , 400 Ġessays Ġlabeled Ġby Ġone Ġof Ġthe Ġfive Ġpersonality Ġtraits Ġeach . </s> The Ġfully - connected Ġlayer Ġhas Ġ150 Ġneurons , Ġwhich Ġare Ġtreated Ġas Ġthe Ġfeatures . ĠWe Ġconc aten ate Ġthe Ġfeature Ġvector Ġof Ġeach Ġpersonality Ġdimension Ġin Ġorder Ġto Ġcreate Ġthe Ġfinal Ġfeature Ġvector . ĠThus , Ġthe Ġpersonality Ġmodel Ġultimately Ġextracts Ġa Ġ750 - dimensional Ġfeature Ġvector Ġ( 150 - dimensional Ġfeature Ġvector Ġfor Ġeach Ġof Ġthe Ġfive Ġpersonality Ġtraits ). ĠThis Ġnetwork Ġis Ġreplicated Ġfive Ġtimes , Ġone Ġfor Ġeach Ġpersonality Ġtrait . ĠIn Ġparticular , Ġwe Ġcreate Ġa ĠCNN Ġfor Ġeach Ġpersonality Ġtrait Ġand Ġthe Ġaim Ġof Ġeach ĠCNN Ġis Ġto Ġclassify Ġa Ġsentence Ġinto Ġbinary Ġclasses , Ġi . e ., Ġwhether Ġit Ġexpresses Ġa Ġpersonality Ġtrait Ġor Ġnot . </s> Bas eline ĠMethod Ġand ĠFeatures </s> CNN Ġcan Ġalso Ġbe Ġemployed Ġon Ġthe Ġsarc asm Ġdatasets Ġin Ġorder Ġto Ġidentify Ġsarcastic Ġand Ġnon - s ar cast ic Ġtweets . ĠWe Ġterm Ġthe Ġfeatures Ġextracted Ġfrom Ġthis Ġnetwork Ġbaseline Ġfeatures , Ġthe Ġmethod Ġas Ġbaseline Ġmethod Ġand Ġthe ĠCNN Ġarchitecture Ġused Ġin Ġthis Ġbaseline Ġmethod Ġas Ġbaseline ĠCNN . ĠSince Ġthe Ġfully - connected Ġlayer Ġhas Ġ100 Ġneurons , Ġwe Ġhave Ġ100 Ġbaseline Ġfeatures Ġin Ġour Ġexperiment . ĠThis Ġmethod Ġis Ġtermed Ġbaseline Ġmethod Ġas Ġit Ġdirectly Ġaims Ġto Ġclassify Ġa Ġsentence Ġas Ġsarcastic Ġvs Ġnon - s ar cast ic . ĠThe Ġbaseline ĠCNN Ġextracts Ġthe Ġinherent Ġsemantics Ġfrom Ġthe Ġsarcastic Ġcorpus Ġby Ġemploying Ġdeep Ġdomain Ġunderstanding . ĠThe Ġprocess Ġof Ġusing Ġbaseline Ġfeatures Ġwith Ġother Ġfeatures Ġextracted Ġfrom Ġthe Ġpre - trained Ġmodel Ġis Ġdescribed Ġin ĠSection ĠSEC REF 24 Ġ. </s> Exper imental ĠResults Ġand ĠDiscussion </s> In Ġthis Ġsection , Ġwe Ġpresent Ġthe Ġexperimental Ġresults Ġusing Ġdifferent Ġfeature Ġcombinations Ġand Ġcompare Ġthem Ġwith Ġthe Ġstate Ġof Ġthe Ġart . ĠFor Ġeach Ġfeature Ġwe Ġshow Ġthe Ġresults Ġusing Ġonly ĠCNN Ġand Ġusing ĠCNN - S VM Ġ( i . e ., Ġwhen Ġthe Ġfeatures Ġextracted Ġby ĠCNN Ġare Ġfed Ġto Ġthe ĠS VM ). ĠMacro - F 1 Ġmeasure Ġis Ġused Ġas Ġan Ġevaluation Ġscheme Ġin Ġthe Ġexperiments . </s> S arc asm ĠDat as ets ĠUsed Ġin Ġthe ĠExperiment </s> This Ġdataset Ġwas Ġcreated Ġby ĠB IB REF 8 Ġ. ĠThe Ġtweets Ġwere Ġdownloaded Ġfrom ĠTwitter Ġusing Ġ# s arc asm Ġas Ġa Ġmarker Ġfor Ġsarcastic Ġtweets . ĠIt Ġis Ġa Ġmon oling ual ĠEnglish Ġdataset Ġwhich Ġconsists Ġof Ġa Ġbalanced Ġdistribution Ġof Ġ50 , 000 Ġsarcastic Ġtweets Ġand Ġ50 , 000 Ġnon - s ar cast ic Ġtweets . </s> Since Ġsarcastic Ġtweets Ġare Ġless Ġfrequently Ġused ĠB IB REF 8 Ġ, Ġwe Ġalso Ġneed Ġto Ġinvestigate Ġthe Ġrobust ness Ġof Ġthe Ġselected Ġfeatures Ġand Ġthe Ġmodel Ġtrained Ġon Ġthese Ġfeatures Ġon Ġan Ġim balanced Ġdataset . ĠTo Ġthis Ġend , Ġwe Ġused Ġanother ĠEnglish Ġdataset Ġfrom ĠB IB REF 8 Ġ. ĠIt Ġconsists Ġof Ġ25 , 000 Ġsarcastic Ġtweets Ġand Ġ75 , 000 Ġnon - s ar cast ic Ġtweets . </s> We Ġhave Ġobtained Ġthis Ġdataset Ġfrom ĠThe ĠS arc asm ĠDet ector . ĠIt Ġcontains Ġ120 , 000 Ġtweets , Ġout Ġof Ġwhich Ġ20 , 000 Ġare Ġsarcastic Ġand Ġ100 , 000 Ġare Ġnon - s ar cast ic . ĠWe Ġrandomly Ġsampled Ġ10 , 000 Ġsarcastic Ġand Ġ20 , 000 Ġnon - s ar cast ic Ġtweets Ġfrom Ġthe Ġdataset . ĠVisual ization Ġof Ġboth Ġthe Ġoriginal Ġand Ġsubset Ġdata Ġshow Ġsimilar Ġcharacteristics . </s> A Ġtwo - step Ġmethodology Ġhas Ġbeen Ġemployed Ġin Ġfiltering Ġthe Ġdatasets Ġused Ġin Ġour Ġexperiments . ĠFirstly , Ġwe Ġidentified Ġand Ġremoved Ġall Ġthe ĠâĢ ľ user ", ĠâĢ ľ URL " Ġand ĠâĢ ľ hash tag " Ġreferences Ġpresent Ġin Ġthe Ġtweets Ġusing Ġefficient Ġregular Ġexpressions . ĠSpecial Ġemphasis Ġwas Ġgiven Ġto Ġthis Ġstep Ġto Ġavoid Ġtraces Ġof Ġhasht ags , Ġwhich Ġmight Ġtrigger Ġthe Ġmodels Ġto Ġprovide Ġbiased Ġresults . ĠSecondly , Ġwe Ġused ĠNL T K ĠTwitter ĠToken izer Ġto Ġensure Ġproper Ġtoken ization Ġof Ġwords Ġalong Ġwith Ġspecial Ġsymbols Ġand Ġemot icons . ĠSince Ġour Ġdeep ĠCNN s Ġextract Ġcontextual Ġinformation Ġpresent Ġin Ġtweets , Ġwe Ġinclude Ġemot icons Ġas Ġpart Ġof Ġthe Ġvocabulary . ĠThis Ġenables Ġthe Ġemot icons Ġto Ġhold Ġa Ġplace Ġin Ġthe Ġword Ġembed ding Ġspace Ġand Ġaid Ġin Ġproviding Ġinformation Ġabout Ġthe Ġemotions Ġpresent Ġin Ġthe Ġsentence . </s> Mer ging Ġthe ĠFeatures </s> Throughout Ġthis Ġresearch , Ġwe Ġhave Ġcarried Ġout Ġseveral Ġexperiments Ġwith Ġvarious Ġfeature Ġcombinations . ĠFor Ġthe Ġsake Ġof Ġclarity , Ġwe Ġexplain Ġbelow Ġhow Ġthe Ġfeatures Ġextracted Ġusing Ġdifference Ġmodels Ġare Ġmerged . </s> In Ġthe Ġstandard Ġfeature Ġmerging Ġprocess , Ġwe Ġfirst Ġextract Ġthe Ġfeatures Ġfrom Ġall Ġdeep ĠCNN Ġbased Ġfeature Ġextraction Ġmodels Ġand Ġthen Ġwe Ġconc aten ate Ġthem . ĠAfterwards , ĠS VM Ġis Ġemployed Ġon Ġthe Ġresulted Ġfeature Ġvector . </s> In Ġanother Ġsetting , Ġwe Ġuse Ġthe Ġfeatures Ġextracted Ġfrom Ġthe Ġpre - trained Ġmodels Ġas Ġthe Ġstatic Ġchannels Ġof Ġfeatures Ġin Ġthe ĠCNN Ġof Ġthe Ġbaseline Ġmethod . ĠThese Ġfeatures Ġare Ġapp ended Ġto Ġthe Ġhidden Ġlayer Ġof Ġthe Ġbaseline ĠCNN , Ġpreceding Ġthe Ġfinal Ġoutput Ġsoft max Ġlayer . </s> For Ġcomparison , Ġwe Ġhave Ġre - im ple mented Ġthe Ġstate - of - the - art Ġmethods . ĠSince ĠB IB REF 9 Ġdid Ġnot Ġmention Ġabout Ġthe Ġsentiment Ġlex icon Ġthey Ġuse Ġin Ġthe Ġexperiment , Ġwe Ġused ĠSent ic Net ĠB IB REF 37 Ġin Ġthe Ġre - im plementation Ġof Ġtheir Ġmethod . </s> Results Ġon ĠDat as et Ġ1 </s> As Ġshown Ġin ĠTable ĠT AB REF 29 Ġ, Ġfor Ġevery Ġfeature ĠCNN - S VM Ġoutper forms Ġthe Ġperformance Ġof Ġthe ĠCNN . ĠFollowing ĠB IB REF 6 Ġ, Ġwe Ġhave Ġcarried Ġout Ġa Ġ5 - fold Ġcross - valid ation Ġon Ġthis Ġdataset . ĠThe Ġbaseline Ġfeatures Ġ( ĠSEC REF 16 Ġ) Ġperform Ġbest Ġamong Ġother Ġfeatures . ĠAmong Ġall Ġthe Ġpre - trained Ġmodels , Ġthe Ġsentiment Ġmodel Ġ( F 1 - score : Ġ87 . 00 %) Ġachieves Ġbetter Ġperformance Ġin Ġcomparison Ġwith Ġthe Ġother Ġtwo Ġpre - trained Ġmodels . ĠInterestingly , Ġwhen Ġwe Ġmerge Ġthe Ġbaseline Ġfeatures Ġwith Ġthe Ġfeatures Ġextracted Ġby Ġthe Ġpre - trained Ġdeep ĠN LP Ġmodels , Ġwe Ġonly Ġget Ġ0 . 11 % Ġimprovement Ġover Ġthe ĠF - score . ĠIt Ġmeans Ġthat Ġthe Ġbaseline Ġfeatures Ġalone Ġare Ġquite Ġcapable Ġto Ġdetect Ġsarc asm . ĠOn Ġthe Ġother Ġhand , Ġwhen Ġwe Ġcombine Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures , Ġwe Ġobtain Ġ90 . 70 % ĠF 1 - score . ĠThis Ġindicates Ġthat Ġthe Ġpre - trained Ġfeatures Ġare Ġindeed Ġuseful Ġfor Ġsarc asm Ġdetection . ĠWe Ġalso Ġcompare Ġour Ġapproach Ġwith Ġthe Ġbest Ġresearch Ġstudy Ġconducted Ġon Ġthis Ġdataset Ġ( Table ĠT AB REF 30 Ġ). ĠBoth Ġthe Ġproposed Ġbaseline Ġmodel Ġand Ġthe Ġbaseline Ġ+ Ġsentiment Ġ+ Ġemotion Ġ+ Ġpersonality Ġmodel Ġoutper form Ġthe Ġstate Ġof Ġthe Ġart ĠB IB REF 9 Ġ, ĠB IB REF 8 Ġ. ĠOne Ġimportant Ġdifference Ġwith Ġthe Ġstate Ġof Ġthe Ġart Ġis Ġthat ĠB IB REF 8 Ġused Ġrelatively Ġlarger Ġfeature Ġvector Ġsize Ġ( ĠIN LINE FORM 0 Ġ500 , 000 ) Ġthan Ġwe Ġused Ġin Ġour Ġexperiment Ġ( 1 , 100 ). ĠThis Ġnot Ġonly Ġprevents Ġour Ġmodel Ġto Ġover fit Ġthe Ġdata Ġbut Ġalso Ġspeeds Ġup Ġthe Ġcomputation . ĠThus , Ġwe Ġobtain Ġan Ġimprovement Ġin Ġthe Ġoverall Ġperformance Ġwith Ġautomatic Ġfeature Ġextraction Ġusing Ġa Ġrelatively Ġlower Ġdimensional Ġfeature Ġspace . </s> In Ġthe Ġliterature , Ġword Ġn - gram s , Ġskip gram s Ġand Ġcharacter Ġn - gram s Ġare Ġused Ġas Ġbaseline Ġfeatures . ĠAccording Ġto ĠPt ace k Ġet Ġal . ĠB IB REF 8 Ġ, Ġthese Ġbaseline Ġfeatures Ġalong Ġwith Ġthe Ġother Ġfeatures Ġ( sent iment Ġfeatures Ġand Ġpart - of - speech Ġbased Ġfeatures ) Ġproduced Ġthe Ġbest Ġperformance . ĠHowever , ĠPt ace k Ġet Ġal . Ġdid Ġnot Ġanalyze Ġthe Ġperformance Ġof Ġthese Ġfeatures Ġwhen Ġthey Ġwere Ġnot Ġused Ġwith Ġthe Ġbaseline Ġfeatures . ĠPre - trained Ġword Ġembed d ings Ġplay Ġan Ġimportant Ġrole Ġin Ġthe Ġperformance Ġof Ġthe Ġclass ifier Ġbecause , Ġwhen Ġwe Ġuse Ġrandomly Ġgenerated Ġembed d ings , Ġperformance Ġfalls Ġdown Ġto Ġ86 . 23 % Ġusing Ġall Ġfeatures . </s> Results Ġon ĠDat as et Ġ2 </s> 5 - fold Ġcross - valid ation Ġhas Ġbeen Ġcarried Ġout Ġon ĠDat as et Ġ2 . ĠAlso Ġfor Ġthis Ġdataset , Ġwe Ġget Ġthe Ġbest Ġaccuracy Ġwhen Ġwe Ġuse Ġall Ġfeatures . ĠBas eline Ġfeatures Ġhave Ġperformed Ġsignificantly Ġbetter Ġ( F 1 - score : Ġ92 . 32 %) Ġthan Ġall Ġother Ġfeatures . ĠSupporting Ġthe Ġobservations Ġwe Ġhave Ġmade Ġfrom Ġthe Ġexperiments Ġon ĠDat as et Ġ1 , Ġwe Ġsee ĠCNN - S VM Ġoutper forming ĠCNN Ġon ĠDat as et Ġ2 . ĠHowever , Ġwhen Ġwe Ġuse Ġall Ġthe Ġfeatures , ĠCNN Ġalone Ġ( F 1 - score : Ġ89 . 73 %) Ġdoes Ġnot Ġoutper form Ġthe Ġstate Ġof Ġthe Ġart ĠB IB REF 8 Ġ( F 1 - score : Ġ92 . 37 %). ĠAs Ġshown Ġin ĠTable ĠT AB REF 30 Ġ, ĠCNN - S VM Ġon Ġthe Ġbaseline Ġ+ Ġsentiment Ġ+ Ġemotion Ġ+ Ġpersonality Ġfeature Ġset Ġoutper forms Ġthe Ġstate Ġof Ġthe Ġart Ġ( F 1 - score : Ġ94 . 80 %). ĠAmong Ġthe Ġpre - trained Ġmodels , Ġthe Ġsentiment Ġmodel Ġperforms Ġbest Ġ( F 1 - score : Ġ87 . 00 %). </s> Table ĠT AB REF 29 Ġshows Ġthe Ġperformance Ġof Ġdifferent Ġfeature Ġcombinations . ĠThe Ġgap Ġbetween Ġthe ĠF 1 - sc ores Ġof Ġonly Ġbaseline Ġfeatures Ġand Ġall Ġfeatures Ġis Ġlarger Ġon Ġthe Ġim balanced Ġdataset Ġthan Ġthe Ġbalanced Ġdataset . ĠThis Ġsupports Ġour Ġclaim Ġthat Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures Ġare Ġvery Ġuseful Ġfor Ġsarc asm Ġdetection , Ġthanks Ġto Ġthe Ġpre - trained Ġmodels . ĠThe ĠF 1 - score Ġusing Ġsentiment Ġfeatures Ġwhen Ġcombined Ġwith Ġbaseline Ġfeatures Ġis Ġ94 . 60 %. ĠOn Ġboth Ġof Ġthe Ġdatasets , Ġemotion Ġand Ġsentiment Ġfeatures Ġperform Ġbetter Ġthan Ġthe Ġpersonality Ġfeatures . ĠInterestingly , Ġusing Ġonly Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures , Ġwe Ġachieve Ġ90 . 90 % ĠF 1 - score . </s> Results Ġon ĠDat as et Ġ3 </s> Exper imental Ġresults Ġon ĠDat as et Ġ3 Ġshow Ġthe Ġsimilar Ġtrends Ġ( Table ĠT AB REF 30 Ġ) Ġas Ġcompared Ġto ĠDat as et Ġ1 Ġand ĠDat as et Ġ2 . ĠThe Ġhighest Ġperformance Ġ( F 1 - score Ġ93 . 30 %) Ġis Ġobtained Ġwhen Ġwe Ġcombine Ġbaseline Ġfeatures Ġwith Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures . ĠIn Ġthis Ġcase , Ġalso ĠCNN - S VM Ġconsistently Ġperforms Ġbetter Ġthan ĠCNN Ġfor Ġevery Ġfeature Ġcombination . ĠThe Ġsentiment Ġmodel Ġis Ġfound Ġto Ġbe Ġthe Ġbest Ġpre - trained Ġmodel . ĠF 1 - score Ġof Ġ84 . 43 % Ġis Ġobtained Ġwhen Ġwe Ġmerge Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures . </s> Dat as et Ġ3 Ġis Ġmore Ġcomplex Ġand Ġnon - linear Ġin Ġnature Ġcompared Ġto Ġthe Ġother Ġtwo Ġdatasets . ĠAs Ġshown Ġin ĠTable ĠT AB REF 30 Ġ, Ġthe Ġmethods Ġby ĠB IB REF 9 Ġand ĠB IB REF 8 Ġperform Ġpoorly Ġon Ġthis Ġdataset . ĠThe ĠTP Ġrate Ġachieved Ġby ĠB IB REF 9 Ġis Ġonly Ġ10 . 07 % Ġand Ġthat Ġmeans Ġtheir Ġmethod Ġsuffers Ġbadly Ġon Ġcomplex Ġdata . ĠThe Ġapproach Ġof ĠB IB REF 8 Ġhas Ġalso Ġfailed Ġto Ġperform Ġwell Ġon ĠDat as et Ġ3 , Ġachieving Ġ62 . 37 % Ġwith Ġa Ġbetter ĠTP Ġrate Ġof Ġ22 . 15 % Ġthan ĠB IB REF 9 Ġ. ĠOn Ġthe Ġother Ġhand , Ġour Ġproposed Ġmodel Ġperforms Ġconsistently Ġwell Ġon Ġthis Ġdataset Ġachieving Ġ93 . 30 %. </s> Testing ĠGeneral iz ability Ġof Ġthe ĠModels Ġand ĠDisc ussions </s> To Ġtest Ġthe Ġgeneral ization Ġcapability Ġof Ġthe Ġproposed Ġapproach , Ġwe Ġperform Ġtraining Ġon ĠDat as et Ġ1 Ġand Ġtest Ġon ĠDat as et Ġ3 . ĠThe ĠF 1 - score Ġdrops Ġdown Ġdramatically Ġto Ġ33 . 05 %. ĠIn Ġorder Ġto Ġunderstand Ġthis Ġfinding , Ġwe Ġvisualize Ġeach Ġdataset Ġusing ĠPC A Ġ( Figure ĠFIG REF 17 Ġ). ĠIt Ġdepicts Ġthat , Ġalthough ĠDat as et Ġ1 Ġis Ġmostly Ġlin early Ġsepar able , ĠDat as et Ġ3 Ġis Ġnot . ĠA Ġlinear Ġkernel Ġthat Ġperforms Ġwell Ġon ĠDat as et Ġ1 Ġfails Ġto Ġprovide Ġgood Ġperformance Ġon ĠDat as et Ġ3 . ĠIf Ġwe Ġuse ĠRB F Ġkernel , Ġit Ġover fits Ġthe Ġdata Ġand Ġproduces Ġworse Ġresults Ġthan Ġwhat Ġwe Ġget Ġusing Ġlinear Ġkernel . ĠSimilar Ġtrends Ġare Ġseen Ġin Ġthe Ġperformance Ġof Ġother Ġtwo Ġstate - of - the - art Ġapproaches ĠB IB REF 9 Ġ, ĠB IB REF 8 Ġ. ĠThus , Ġwe Ġdecide Ġto Ġperform Ġtraining Ġon ĠDat as et Ġ3 Ġand Ġtest Ġon Ġthe ĠDat as et Ġ1 . ĠAs Ġexpected Ġbetter Ġperformance Ġis Ġobtained Ġwith ĠF 1 - score Ġ76 . 78 %. ĠHowever , Ġthe Ġother Ġtwo Ġstate - of - the - art Ġapproaches Ġfail Ġto Ġperform Ġwell Ġin Ġthis Ġsetting . ĠWhile Ġthe Ġmethod Ġby ĠB IB REF 9 Ġob tains ĠF 1 - score Ġof Ġ47 . 32 %, Ġthe Ġapproach Ġby ĠB IB REF 8 Ġachieves Ġ53 . 02 % ĠF 1 - score Ġwhen Ġtrained Ġon ĠDat as et Ġ3 Ġand Ġtested Ġon ĠDat as et Ġ1 . ĠBelow , Ġwe Ġdiscuss Ġabout Ġthis Ġgeneral iz ability Ġissue Ġof Ġthe Ġmodels Ġdeveloped Ġor Ġreferred Ġin Ġthis Ġpaper . </s> As Ġdiscussed Ġin Ġthe Ġintroduction , Ġsarc asm Ġis Ġvery Ġmuch Ġtopic - dependent Ġand Ġhighly Ġcontextual . ĠFor Ġexample , Ġlet Ġus Ġconsider Ġthe Ġtweet ĠâĢ ľ I Ġam Ġso Ġglad Ġto Ġsee ĠTanzania Ġplayed Ġvery Ġwell , ĠI Ġcan Ġnow Ġsleep Ġwell Ġ: P ". ĠUnless Ġone Ġknows Ġthat ĠTanzania Ġactually Ġdid Ġnot Ġplay Ġwell Ġin Ġthat Ġgame , Ġit Ġis Ġnot Ġpossible Ġto Ġspot Ġthe Ġsarcastic Ġnature Ġof Ġthis Ġsentence . ĠThus , Ġan Ġn - gram Ġbased Ġsarc asm Ġdetector Ġtrained Ġat Ġtime ĠIN LINE FORM 0 Ġmay Ġperform Ġpoorly Ġto Ġdetect Ġsarc asm Ġin Ġthe Ġtweets Ġcrawled Ġat Ġtime ĠIN LINE FORM 1 Ġ( given Ġthat Ġthere Ġis Ġa Ġconsiderable Ġgap Ġbetween Ġthese Ġtime Ġstamps ) Ġbecause Ġof Ġthe Ġdiversity Ġof Ġthe Ġtopics Ġ( new Ġevents Ġoccur , Ġnew Ġtopics Ġare Ġdiscussed ) Ġof Ġthe Ġtweets . ĠSent iment Ġand Ġother Ġcontextual Ġclues Ġcan Ġhelp Ġto Ġspot Ġthe Ġsarcastic Ġnature Ġin Ġthis Ġkind Ġof Ġtweets . ĠA Ġhighly Ġpositive Ġstatement Ġwhich Ġends Ġwith Ġa Ġemot icon Ġexpressing Ġjoke Ġcan Ġbe Ġsarcastic . </s> State - of - the - art Ġmethods Ġlack Ġthese Ġcontextual Ġinformation Ġwhich , Ġin Ġour Ġcase , Ġwe Ġextract Ġusing Ġpre - trained Ġsentiment , Ġemotion Ġand Ġpersonality Ġmodels . ĠNot Ġonly Ġthese Ġpre - trained Ġmodels , Ġthe Ġbaseline Ġmethod Ġ( bas eline ĠCNN Ġarchitecture ) Ġperforms Ġbetter Ġthan Ġthe Ġstate - of - the - art Ġmodels Ġin Ġthis Ġgeneral iz ability Ġtest Ġsetting . ĠIn Ġour Ġgeneral iz ability Ġtest , Ġwhen Ġthe Ġpre - trained Ġfeatures Ġare Ġused Ġwith Ġbaseline Ġfeatures , Ġwe Ġget Ġ4 . 19 % ĠF 1 - score Ġimprovement Ġover Ġthe Ġbaseline Ġfeatures . ĠOn Ġthe Ġother Ġhand , Ġwhen Ġthey Ġare Ġnot Ġused Ġwith Ġthe Ġbaseline Ġfeatures , Ġtogether Ġthey Ġproduce Ġ64 . 25 % ĠF 1 - score . </s> Another Ġimportant Ġfact Ġis Ġthat Ġan Ġn - gram s Ġmodel Ġcannot Ġperform Ġwell Ġon Ġunseen Ġdata Ġunless Ġit Ġis Ġtrained Ġon Ġa Ġvery Ġlarge Ġcorpus . ĠIf Ġmost Ġof Ġthe Ġn - gram s Ġextracted Ġfrom Ġthe Ġunseen Ġdata Ġare Ġnot Ġin Ġthe Ġvocabulary Ġof Ġthe Ġalready Ġtrained Ġn - gram s Ġmodel , Ġin Ġfact , Ġthe Ġmodel Ġwill Ġproduce Ġa Ġvery Ġsparse Ġfeature Ġvector Ġrepresentation Ġof Ġthe Ġdataset . ĠInstead , Ġwe Ġuse Ġthe Ġword 2 vec Ġembed d ings Ġas Ġthe Ġsource Ġof Ġthe Ġfeatures , Ġas Ġword 2 vec Ġallows Ġfor Ġthe Ġcomputation Ġof Ġsimilarities Ġbetween Ġunseen Ġdata Ġand Ġtraining Ġdata . </s> Bas eline ĠFeatures Ġvs ĠPre - trained ĠFeatures </s> Our Ġexperimental Ġresults Ġshow Ġthat Ġthe Ġbaseline Ġfeatures Ġoutper form Ġthe Ġpre - trained Ġfeatures Ġfor Ġsarc asm Ġdetection . ĠHowever , Ġthe Ġcombination Ġof Ġpre - trained Ġfeatures Ġand Ġbaseline Ġfeatures Ġbeats Ġboth Ġof Ġthemselves Ġalone . ĠIt Ġis Ġcounter intuitive , Ġsince Ġexperimental Ġresults Ġprove Ġthat Ġboth Ġof Ġthose Ġfeatures Ġlearn Ġalmost Ġthe Ġsame Ġglobal Ġand Ġcontextual Ġfeatures . ĠIn Ġparticular , Ġbaseline Ġnetwork Ġdominates Ġover Ġpre - trained Ġnetwork Ġas Ġthe Ġformer Ġlearns Ġmost Ġof Ġthe Ġfeatures Ġlearned Ġby Ġthe Ġlatter . ĠNonetheless , Ġthe Ġcombination Ġof Ġbaseline Ġand Ġpre - trained Ġclass ifiers Ġimproves Ġthe Ġoverall Ġperformance Ġand Ġgeneral iz ability , Ġhence Ġproving Ġtheir Ġeffectiveness Ġin Ġsarc asm Ġdetection . ĠExperimental Ġresults Ġshow Ġthat Ġsentiment Ġand Ġemotion Ġfeatures Ġare Ġthe Ġmost Ġuseful Ġfeatures , Ġbesides Ġbaseline Ġfeatures Ġ( Figure ĠFIG REF 36 Ġ). ĠTherefore , Ġin Ġorder Ġto Ġreach Ġa Ġbetter Ġunderstanding Ġof Ġthe Ġrelation Ġbetween Ġpersonality Ġfeatures Ġamong Ġthemselves Ġand Ġwith Ġother Ġpre - trained Ġfeatures , Ġwe Ġcarried Ġout ĠSpear man Ġcorrelation Ġtesting . ĠResults , Ġdisplayed Ġin ĠTable ĠT AB REF 39 Ġ, Ġshow Ġthat Ġthose Ġfeatures Ġare Ġhighly Ġcorrelated Ġwith Ġeach Ġother . </s> Conclusion </s> In Ġthis Ġwork , Ġwe Ġdeveloped Ġpre - trained Ġsentiment , Ġemotion Ġand Ġpersonality Ġmodels Ġfor Ġidentifying Ġsarcastic Ġtext Ġusing ĠCNN , Ġwhich Ġare Ġfound Ġto Ġbe Ġvery Ġeffective Ġfor Ġsarc asm Ġdetection . ĠIn Ġthe Ġfuture , Ġwe Ġplan Ġto Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġproposed Ġmethod Ġon Ġa Ġlarge Ġcorpus Ġand Ġother Ġdomain - dependent Ġcorpor a . ĠFuture Ġwork Ġwill Ġalso Ġfocus Ġon Ġanalyzing Ġpast Ġtweets Ġand Ġactivities Ġof Ġusers Ġin Ġorder Ġto Ġbetter Ġunderstand Ġtheir Ġpersonality Ġand Ġprofile Ġand , Ġhence , Ġfurther Ġimprove Ġthe Ġdis amb ig uation Ġbetween Ġsarcastic Ġand Ġnon - s ar cast ic Ġtext .
Introduction Sarcasm is defined as “a sharp, bitter, or cutting expression or remark; a bitter gibe or taunt”. As the fields of affective computing and sentiment analysis have gained increasing popularity BIBREF0 , it is a major concern to detect sarcastic, ironic, and metaphoric expressions. Sarcasm, especially, is key for sentiment analysis as it can completely flip the polarity of opinions. Understanding the ground truth, or the facts about a given event, allows for the detection of contradiction between the objective polarity of the event (usually negative) and its sarcastic characteristic by the author (usually positive), as in “I love the pain of breakup”. Obtaining such knowledge is, however, very difficult. In our experiments, we exposed the classifier to such knowledge extracted indirectly from Twitter. Namely, we used Twitter data crawled in a time period, which likely contain both the sarcastic and non-sarcastic accounts of an event or similar events. We believe that unambiguous non-sarcastic sentences provided the classifier with the ground-truth polarity of those events, which the classifier could then contrast with the opposite estimations in sarcastic sentences. Twitter is a more suitable resource for this purpose than blog posts, because the polarity of short tweets is easier to detect (as all the information necessary to detect polarity is likely to be contained in the same sentence) and because the Twitter API makes it easy to collect a large corpus of tweets containing both sarcastic and non-sarcastic examples of the same event. Sometimes, however, just knowing the ground truth or simple facts on the topic is not enough, as the text may refer to other events in order to express sarcasm. For example, the sentence “If Hillary wins, she will surely be pleased to recall Monica each time she enters the Oval Office :P :D”, which refers to the 2016 US presidential election campaign and to the events of early 1990's related to the US president Clinton, is sarcastic because Hillary, a candidate and Clinton's wife, would in fact not be pleased to recall her husband's alleged past affair with Monica Lewinsky. The system, however, would need a considerable amount of facts, commonsense knowledge, anaphora resolution, and logical reasoning to draw such a conclusion. In this paper, we will not deal with such complex cases. Existing works on sarcasm detection have mainly focused on unigrams and the use of emoticons BIBREF1 , BIBREF2 , BIBREF3 , unsupervised pattern mining approach BIBREF4 , semi-supervised approach BIBREF5 and n-grams based approach BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 with sentiment features. Instead, we propose a framework that learns sarcasm features automatically from a sarcasm corpus using a convolutional neural network (CNN). We also investigate whether features extracted using the pre-trained sentiment, emotion and personality models can improve sarcasm detection performance. Our approach uses relatively lower dimensional feature vectors and outperforms the state of the art on different datasets. In summary, the main contributions of this paper are the following: The rest of the paper is organized as follows: Section SECREF2 proposes a brief literature review on sarcasm detection; Section SECREF4 presents the proposed approach; experimental results and thorough discussion on the experiments are given in Section SECREF5 ; finally, Section SECREF6 concludes the paper. Related Works NLP research is gradually evolving from lexical to compositional semantics BIBREF10 through the adoption of novel meaning-preserving and context-aware paradigms such as convolutional networks BIBREF11 , recurrent belief networks BIBREF12 , statistical learning theory BIBREF13 , convolutional multiple kernel learning BIBREF14 , and commonsense reasoning BIBREF15 . But while other NLP tasks have been extensively investigated, sarcasm detection is a relatively new research topic which has gained increasing interest only recently, partly thanks to the rise of social media analytics and sentiment analysis. Sentiment analysis BIBREF16 and using multimodal information as a new trend BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF14 is a popular branch of NLP research that aims to understand sentiment of documents automatically using combination of various machine learning approaches BIBREF21 , BIBREF22 , BIBREF20 , BIBREF23 . An early work in this field was done by BIBREF6 on a dataset of 6,600 manually annotated Amazon reviews using a kNN-classifier over punctuation-based and pattern-based features, i.e., ordered sequence of high frequency words. BIBREF1 used support vector machine (SVM) and logistic regression over a feature set of unigrams, dictionary-based lexical features and pragmatic features (e.g., emoticons) and compared the performance of the classifier with that of humans. BIBREF24 described a set of textual features for recognizing irony at a linguistic level, especially in short texts created via Twitter, and constructed a new model that was assessed along two dimensions: representativeness and relevance. BIBREF5 used the presence of a positive sentiment in close proximity of a negative situation phrase as a feature for sarcasm detection. BIBREF25 used the Balanced Window algorithm for classifying Dutch tweets as sarcastic vs. non-sarcastic; n-grams (uni, bi and tri) and intensifiers were used as features for classification. BIBREF26 compared the performance of different classifiers on the Amazon review dataset using the imbalance between the sentiment expressed by the review and the user-given star rating. Features based on frequency (gap between rare and common words), written spoken gap (in terms of difference between usage), synonyms (based on the difference in frequency of synonyms) and ambiguity (number of words with many synonyms) were used by BIBREF3 for sarcasm detection in tweets. BIBREF9 proposed the use of implicit incongruity and explicit incongruity based features along with lexical and pragmatic features, such as emoticons and punctuation marks. Their method is very much similar to the method proposed by BIBREF5 except BIBREF9 used explicit incongruity features. Their method outperforms the approach by BIBREF5 on two datasets. BIBREF8 compared the performance with different language-independent features and pre-processing techniques for classifying text as sarcastic and non-sarcastic. The comparison was done over three Twitter dataset in two different languages, two of these in English with a balanced and an imbalanced distribution and the third one in Czech. The feature set included n-grams, word-shape patterns, pointedness and punctuation-based features. In this work, we use features extracted from a deep CNN for sarcasm detection. Some of the key differences between the proposed approach and existing methods include the use of a relatively smaller feature set, automatic feature extraction, the use of deep networks, and the adoption of pre-trained NLP models. Sentiment Analysis and Sarcasm Detection Sarcasm detection is an important subtask of sentiment analysis BIBREF27 . Since sarcastic sentences are subjective, they carry sentiment and emotion-bearing information. Most of the studies in the literature BIBREF28 , BIBREF29 , BIBREF9 , BIBREF30 include sentiment features in sarcasm detection with the use of a state-of-the-art sentiment lexicon. Below, we explain how sentiment information is key to express sarcastic opinions and the approach we undertake to exploit such information for sarcasm detection. In general, most sarcastic sentences contradict the fact. In the sentence “I love the pain present in the breakups" (Figure FIGREF4 ), for example, the word “love" contradicts “pain present in the breakups”, because in general no-one loves to be in pain. In this case, the fact (i.e., “pain in the breakups") and the contradictory statement to that fact (i.e., “I love") express sentiment explicitly. Sentiment shifts from positive to negative but, according to sentic patterns BIBREF31 , the literal sentiment remains positive. Sentic patterns, in fact, aim to detect the polarity expressed by the speaker; thus, whenever the construction “I love” is encountered, the sentence is positive no matter what comes after it (e.g., “I love the movie that you hate”). In this case, however, the sentence carries sarcasm and, hence, reflects the negative sentiment of the speaker. In another example (Figure FIGREF4 ), the fact, i.e., “I left the theater during the interval", has implicit negative sentiment. The statement “I love the movie" contradicts the fact “I left the theater during the interval"; thus, the sentence is sarcastic. Also in this case the sentiment shifts from positive to negative and hints at the sarcastic nature of the opinion. The above discussion has made clear that sentiment (and, in particular, sentiment shifts) can largely help to detect sarcasm. In order to include sentiment shifting into the proposed framework, we train a sentiment model for sentiment-specific feature extraction. Training with a CNN helps to combine the local features in the lower layers into global features in the higher layers. We do not make use of sentic patterns BIBREF31 in this paper but we do plan to explore that research direction as a part of our future work. In the literature, it is found that sarcasm is user-specific too, i.e., some users have a particular tendency to post more sarcastic tweets than others. This acts as a primary intuition for us to extract personality-based features for sarcasm detection. The Proposed Framework As discussed in the literature BIBREF5 , sarcasm detection may depend on sentiment and other cognitive aspects. For this reason, we incorporate both sentiment and emotion clues in our framework. Along with these, we also argue that personality of the opinion holder is an important factor for sarcasm detection. In order to address all of these variables, we create different models for each of them, namely: sentiment, emotion and personality. The idea is to train each model on its corresponding benchmark dataset and, hence, use such pre-trained models together to extract sarcasm-related features from the sarcasm datasets. Now, the viable research question here is - Do these models help to improve sarcasm detection performance?' Literature shows that they improve the performance but not significantly. Thus, do we need to consider those factors in spotting sarcastic sentences? Aren't n-grams enough for sarcasm detection? Throughout the rest of this paper, we address these questions in detail. The training of each model is done using a CNN. Below, we explain the framework in detail. Then, we discuss the pre-trained models. Figure FIGREF6 presents a visualization of the proposed framework. General CNN Framework CNN can automatically extract key features from the training data. It grasps contextual local features from a sentence and, after several convolution operations, it forms a global feature vector out of those local features. CNN does not need the hand-crafted features used in traditional supervised classifiers. Such hand-crafted features are difficult to compute and a good guess for encoding the features is always necessary in order to get satisfactory results. CNN, instead, uses a hierarchy of local features which are important to learn context. The hand-crafted features often ignore such a hierarchy of local features. Features extracted by CNN can therefore be used instead of hand-crafted features, as they carry more useful information. The idea behind convolution is to take the dot product of a vector of INLINEFORM0 weights INLINEFORM1 also known as kernel vector with each INLINEFORM2 -gram in the sentence INLINEFORM3 to obtain another sequence of features INLINEFORM4 . DISPLAYFORM0 Thus, a max pooling operation is applied over the feature map and the maximum value INLINEFORM0 is taken as the feature corresponding to this particular kernel vector. Similarly, varying kernel vectors and window sizes are used to obtain multiple features BIBREF32 . For each word INLINEFORM1 in the vocabulary, a INLINEFORM2 -dimensional vector representation is given in a look up table that is learned from the data BIBREF33 . The vector representation of a sentence, hence, is a concatenation of vectors for individual words. Similarly, we can have look up tables for other features. One might want to provide features other than words if these features are suspected to be helpful. The convolution kernels are then applied to word vectors instead of individual words. We use these features to train higher layers of the CNN, in order to represent bigger groups of words in sentences. We denote the feature learned at hidden neuron INLINEFORM0 in layer INLINEFORM1 as INLINEFORM2 . Multiple features may be learned in parallel in the same CNN layer. The features learned in each layer are used to train the next layer: DISPLAYFORM0 where * indicates convolution and INLINEFORM0 is a weight kernel for hidden neuron INLINEFORM1 and INLINEFORM2 is the total number of hidden neurons. The CNN sentence model preserves the order of words by adopting convolution kernels of gradually increasing sizes that span an increasing number of words and ultimately the entire sentence. As mentioned above, each word in a sentence is represented using word embeddings. We employ the publicly available word2vec vectors, which were trained on 100 billion words from Google News. The vectors are of dimensionality 300, trained using the continuous bag-of-words architecture BIBREF33 . Words not present in the set of pre-trained words are initialized randomly. However, while training the neural network, we use non-static representations. These include the word vectors, taken as input, into the list of parameters to be learned during training. Two primary reasons motivated us to use non-static channels as opposed to static ones. Firstly, the common presence of informal language and words in tweets resulted in a relatively high random initialization of word vectors due to the unavailability of these words in the word2vec dictionary. Secondly, sarcastic sentences are known to include polarity shifts in sentimental and emotional degrees. For example, “I love the pain present in breakups" is a sarcastic sentence with a significant change in sentimental polarity. As word2vec was not trained to incorporate these nuances, we allow our models to update the embeddings during training in order to include them. Each sentence is wrapped to a window of INLINEFORM0 , where INLINEFORM1 is the maximum number of words amongst all sentences in the dataset. We use the output of the fully-connected layer of the network as our feature vector. We have done two kinds of experiments: firstly, we used CNN for the classification; secondly, we extracted features from the fully-connected layer of the CNN and fed them to an SVM for the final classification. The latter CNN-SVM scheme is quite useful for text classification as shown by Poria et al. BIBREF18 . We carry out n-fold cross-validation on the dataset using CNN. In every fold iteration, in order to obtain the training and test features, the output of the fully-connected layer is treated as features to be used for the final classification using SVM. Table TABREF12 shows the training settings for each CNN model developed in this work. ReLU is used as the non-linear activation function of the network. The network configurations of all models developed in this work are given in Table TABREF12 . Sentiment Feature Extraction Model As discussed above, sentiment clues play an important role for sarcastic sentence detection. In our work, we train a CNN (see Section SECREF5 for details) on a sentiment benchmark dataset. This pre-trained model is then used to extract features from the sarcastic datasets. In particular, we use Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset for the training. This dataset contains 9,497 tweets out of which 5,895 are positive, 3,131 are negative and 471 are neutral. The fully-connected layer of the CNN used for sentiment feature extraction has 100 neurons, so 100 features are extracted from this pre-trained model. The final softmax determines whether a sentence is positive, negative or neutral. Thus, we have three neurons in the softmax layer. Emotion Feature Extraction Model We use the CNN structure as described in Section SECREF5 for emotional feature extraction. As a dataset for extracting emotion-related features, we use the corpus developed by BIBREF35 . This dataset consists of blog posts labeled by their corresponding emotion categories. As emotion taxonomy, the authors used six basic emotions, i.e., Anger, Disgust, Surprise, Sadness, Joy and Fear. In particular, the blog posts were split into sentences and each sentence was labeled. The dataset contains 5,205 sentences labeled by one of the emotion labels. After employing this model on the sarcasm dataset, we obtained a 150-dimensional feature vector from the fully-connected layer. As the aim of training is to classify each sentence into one of the six emotion classes, we used six neurons in the softmax layer. Personality Feature Extraction Model Detecting personality from text is a well-known challenging problem. In our work, we use five personality traits described by BIBREF36 , i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, sometimes abbreviated as OCEAN (by their first letters). As a training dataset, we use the corpus developed by BIBREF36 , which contains 2,400 essays labeled by one of the five personality traits each. The fully-connected layer has 150 neurons, which are treated as the features. We concatenate the feature vector of each personality dimension in order to create the final feature vector. Thus, the personality model ultimately extracts a 750-dimensional feature vector (150-dimensional feature vector for each of the five personality traits). This network is replicated five times, one for each personality trait. In particular, we create a CNN for each personality trait and the aim of each CNN is to classify a sentence into binary classes, i.e., whether it expresses a personality trait or not. Baseline Method and Features CNN can also be employed on the sarcasm datasets in order to identify sarcastic and non-sarcastic tweets. We term the features extracted from this network baseline features, the method as baseline method and the CNN architecture used in this baseline method as baseline CNN. Since the fully-connected layer has 100 neurons, we have 100 baseline features in our experiment. This method is termed baseline method as it directly aims to classify a sentence as sarcastic vs non-sarcastic. The baseline CNN extracts the inherent semantics from the sarcastic corpus by employing deep domain understanding. The process of using baseline features with other features extracted from the pre-trained model is described in Section SECREF24 . Experimental Results and Discussion In this section, we present the experimental results using different feature combinations and compare them with the state of the art. For each feature we show the results using only CNN and using CNN-SVM (i.e., when the features extracted by CNN are fed to the SVM). Macro-F1 measure is used as an evaluation scheme in the experiments. Sarcasm Datasets Used in the Experiment This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets. Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets. We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics. A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user", “URL" and “hashtag" references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence. Merging the Features Throughout this research, we have carried out several experiments with various feature combinations. For the sake of clarity, we explain below how the features extracted using difference models are merged. In the standard feature merging process, we first extract the features from all deep CNN based feature extraction models and then we concatenate them. Afterwards, SVM is employed on the resulted feature vector. In another setting, we use the features extracted from the pre-trained models as the static channels of features in the CNN of the baseline method. These features are appended to the hidden layer of the baseline CNN, preceding the final output softmax layer. For comparison, we have re-implemented the state-of-the-art methods. Since BIBREF9 did not mention about the sentiment lexicon they use in the experiment, we used SenticNet BIBREF37 in the re-implementation of their method. Results on Dataset 1 As shown in Table TABREF29 , for every feature CNN-SVM outperforms the performance of the CNN. Following BIBREF6 , we have carried out a 5-fold cross-validation on this dataset. The baseline features ( SECREF16 ) perform best among other features. Among all the pre-trained models, the sentiment model (F1-score: 87.00%) achieves better performance in comparison with the other two pre-trained models. Interestingly, when we merge the baseline features with the features extracted by the pre-trained deep NLP models, we only get 0.11% improvement over the F-score. It means that the baseline features alone are quite capable to detect sarcasm. On the other hand, when we combine sentiment, emotion and personality features, we obtain 90.70% F1-score. This indicates that the pre-trained features are indeed useful for sarcasm detection. We also compare our approach with the best research study conducted on this dataset (Table TABREF30 ). Both the proposed baseline model and the baseline + sentiment + emotion + personality model outperform the state of the art BIBREF9 , BIBREF8 . One important difference with the state of the art is that BIBREF8 used relatively larger feature vector size ( INLINEFORM0 500,000) than we used in our experiment (1,100). This not only prevents our model to overfit the data but also speeds up the computation. Thus, we obtain an improvement in the overall performance with automatic feature extraction using a relatively lower dimensional feature space. In the literature, word n-grams, skipgrams and character n-grams are used as baseline features. According to Ptacek et al. BIBREF8 , these baseline features along with the other features (sentiment features and part-of-speech based features) produced the best performance. However, Ptacek et al. did not analyze the performance of these features when they were not used with the baseline features. Pre-trained word embeddings play an important role in the performance of the classifier because, when we use randomly generated embeddings, performance falls down to 86.23% using all features. Results on Dataset 2 5-fold cross-validation has been carried out on Dataset 2. Also for this dataset, we get the best accuracy when we use all features. Baseline features have performed significantly better (F1-score: 92.32%) than all other features. Supporting the observations we have made from the experiments on Dataset 1, we see CNN-SVM outperforming CNN on Dataset 2. However, when we use all the features, CNN alone (F1-score: 89.73%) does not outperform the state of the art BIBREF8 (F1-score: 92.37%). As shown in Table TABREF30 , CNN-SVM on the baseline + sentiment + emotion + personality feature set outperforms the state of the art (F1-score: 94.80%). Among the pre-trained models, the sentiment model performs best (F1-score: 87.00%). Table TABREF29 shows the performance of different feature combinations. The gap between the F1-scores of only baseline features and all features is larger on the imbalanced dataset than the balanced dataset. This supports our claim that sentiment, emotion and personality features are very useful for sarcasm detection, thanks to the pre-trained models. The F1-score using sentiment features when combined with baseline features is 94.60%. On both of the datasets, emotion and sentiment features perform better than the personality features. Interestingly, using only sentiment, emotion and personality features, we achieve 90.90% F1-score. Results on Dataset 3 Experimental results on Dataset 3 show the similar trends (Table TABREF30 ) as compared to Dataset 1 and Dataset 2. The highest performance (F1-score 93.30%) is obtained when we combine baseline features with sentiment, emotion and personality features. In this case, also CNN-SVM consistently performs better than CNN for every feature combination. The sentiment model is found to be the best pre-trained model. F1-score of 84.43% is obtained when we merge sentiment, emotion and personality features. Dataset 3 is more complex and non-linear in nature compared to the other two datasets. As shown in Table TABREF30 , the methods by BIBREF9 and BIBREF8 perform poorly on this dataset. The TP rate achieved by BIBREF9 is only 10.07% and that means their method suffers badly on complex data. The approach of BIBREF8 has also failed to perform well on Dataset 3, achieving 62.37% with a better TP rate of 22.15% than BIBREF9 . On the other hand, our proposed model performs consistently well on this dataset achieving 93.30%. Testing Generalizability of the Models and Discussions To test the generalization capability of the proposed approach, we perform training on Dataset 1 and test on Dataset 3. The F1-score drops down dramatically to 33.05%. In order to understand this finding, we visualize each dataset using PCA (Figure FIGREF17 ). It depicts that, although Dataset 1 is mostly linearly separable, Dataset 3 is not. A linear kernel that performs well on Dataset 1 fails to provide good performance on Dataset 3. If we use RBF kernel, it overfits the data and produces worse results than what we get using linear kernel. Similar trends are seen in the performance of other two state-of-the-art approaches BIBREF9 , BIBREF8 . Thus, we decide to perform training on Dataset 3 and test on the Dataset 1. As expected better performance is obtained with F1-score 76.78%. However, the other two state-of-the-art approaches fail to perform well in this setting. While the method by BIBREF9 obtains F1-score of 47.32%, the approach by BIBREF8 achieves 53.02% F1-score when trained on Dataset 3 and tested on Dataset 1. Below, we discuss about this generalizability issue of the models developed or referred in this paper. As discussed in the introduction, sarcasm is very much topic-dependent and highly contextual. For example, let us consider the tweet “I am so glad to see Tanzania played very well, I can now sleep well :P". Unless one knows that Tanzania actually did not play well in that game, it is not possible to spot the sarcastic nature of this sentence. Thus, an n-gram based sarcasm detector trained at time INLINEFORM0 may perform poorly to detect sarcasm in the tweets crawled at time INLINEFORM1 (given that there is a considerable gap between these time stamps) because of the diversity of the topics (new events occur, new topics are discussed) of the tweets. Sentiment and other contextual clues can help to spot the sarcastic nature in this kind of tweets. A highly positive statement which ends with a emoticon expressing joke can be sarcastic. State-of-the-art methods lack these contextual information which, in our case, we extract using pre-trained sentiment, emotion and personality models. Not only these pre-trained models, the baseline method (baseline CNN architecture) performs better than the state-of-the-art models in this generalizability test setting. In our generalizability test, when the pre-trained features are used with baseline features, we get 4.19% F1-score improvement over the baseline features. On the other hand, when they are not used with the baseline features, together they produce 64.25% F1-score. Another important fact is that an n-grams model cannot perform well on unseen data unless it is trained on a very large corpus. If most of the n-grams extracted from the unseen data are not in the vocabulary of the already trained n-grams model, in fact, the model will produce a very sparse feature vector representation of the dataset. Instead, we use the word2vec embeddings as the source of the features, as word2vec allows for the computation of similarities between unseen data and training data. Baseline Features vs Pre-trained Features Our experimental results show that the baseline features outperform the pre-trained features for sarcasm detection. However, the combination of pre-trained features and baseline features beats both of themselves alone. It is counterintuitive, since experimental results prove that both of those features learn almost the same global and contextual features. In particular, baseline network dominates over pre-trained network as the former learns most of the features learned by the latter. Nonetheless, the combination of baseline and pre-trained classifiers improves the overall performance and generalizability, hence proving their effectiveness in sarcasm detection. Experimental results show that sentiment and emotion features are the most useful features, besides baseline features (Figure FIGREF36 ). Therefore, in order to reach a better understanding of the relation between personality features among themselves and with other pre-trained features, we carried out Spearman correlation testing. Results, displayed in Table TABREF39 , show that those features are highly correlated with each other. Conclusion In this work, we developed pre-trained sentiment, emotion and personality models for identifying sarcastic text using CNN, which are found to be very effective for sarcasm detection. In the future, we plan to evaluate the performance of the proposed method on a large corpus and other domain-dependent corpora. Future work will also focus on analyzing past tweets and activities of users in order to better understand their personality and profile and, hence, further improve the disambiguation between sarcastic and non-sarcastic text.
What are the state of the art models?
3a6e843c6c81244c14730295cfb8b865cd7ede46
What Ġare Ġthe Ġstate Ġof Ġthe Ġart Ġmodels ?
BI B REF 9 Ġ, ĠB IB REF 8 Ġ
<s> What Ġare Ġthe Ġstate Ġof Ġthe Ġart Ġmodels ? </s> Introduction </s> S arc asm Ġis Ġdefined Ġas ĠâĢ ľ a Ġsharp , Ġbitter , Ġor Ġcutting Ġexpression Ġor Ġremark ; Ġa Ġbitter Ġg ibe Ġor Ġtaunt âĢ Ŀ . ĠAs Ġthe Ġfields Ġof Ġaffect ive Ġcomputing Ġand Ġsentiment Ġanalysis Ġhave Ġgained Ġincreasing Ġpopularity ĠB IB REF 0 Ġ, Ġit Ġis Ġa Ġmajor Ġconcern Ġto Ġdetect Ġsarcastic , Ġironic , Ġand Ġmetaph oric Ġexpressions . ĠS arc asm , Ġespecially , Ġis Ġkey Ġfor Ġsentiment Ġanalysis Ġas Ġit Ġcan Ġcompletely Ġflip Ġthe Ġpol arity Ġof Ġopinions . ĠUnderstanding Ġthe Ġground Ġtruth , Ġor Ġthe Ġfacts Ġabout Ġa Ġgiven Ġevent , Ġallows Ġfor Ġthe Ġdetection Ġof Ġcontradiction Ġbetween Ġthe Ġobjective Ġpol arity Ġof Ġthe Ġevent Ġ( usually Ġnegative ) Ġand Ġits Ġsarcastic Ġcharacteristic Ġby Ġthe Ġauthor Ġ( usually Ġpositive ), Ġas Ġin ĠâĢ ľ I Ġlove Ġthe Ġpain Ġof Ġbreakup âĢ Ŀ . ĠOb taining Ġsuch Ġknowledge Ġis , Ġhowever , Ġvery Ġdifficult . </s> In Ġour Ġexperiments , Ġwe Ġexposed Ġthe Ġclass ifier Ġto Ġsuch Ġknowledge Ġextracted Ġindirectly Ġfrom ĠTwitter . ĠName ly , Ġwe Ġused ĠTwitter Ġdata Ġcrawled Ġin Ġa Ġtime Ġperiod , Ġwhich Ġlikely Ġcontain Ġboth Ġthe Ġsarcastic Ġand Ġnon - s ar cast ic Ġaccounts Ġof Ġan Ġevent Ġor Ġsimilar Ġevents . ĠWe Ġbelieve Ġthat Ġunamb iguous Ġnon - s ar cast ic Ġsentences Ġprovided Ġthe Ġclass ifier Ġwith Ġthe Ġground - truth Ġpol arity Ġof Ġthose Ġevents , Ġwhich Ġthe Ġclass ifier Ġcould Ġthen Ġcontrast Ġwith Ġthe Ġopposite Ġestim ations Ġin Ġsarcastic Ġsentences . ĠTwitter Ġis Ġa Ġmore Ġsuitable Ġresource Ġfor Ġthis Ġpurpose Ġthan Ġblog Ġposts , Ġbecause Ġthe Ġpol arity Ġof Ġshort Ġtweets Ġis Ġeasier Ġto Ġdetect Ġ( as Ġall Ġthe Ġinformation Ġnecessary Ġto Ġdetect Ġpol arity Ġis Ġlikely Ġto Ġbe Ġcontained Ġin Ġthe Ġsame Ġsentence ) Ġand Ġbecause Ġthe ĠTwitter ĠAPI Ġmakes Ġit Ġeasy Ġto Ġcollect Ġa Ġlarge Ġcorpus Ġof Ġtweets Ġcontaining Ġboth Ġsarcastic Ġand Ġnon - s ar cast ic Ġexamples Ġof Ġthe Ġsame Ġevent . </s> Sometimes , Ġhowever , Ġjust Ġknowing Ġthe Ġground Ġtruth Ġor Ġsimple Ġfacts Ġon Ġthe Ġtopic Ġis Ġnot Ġenough , Ġas Ġthe Ġtext Ġmay Ġrefer Ġto Ġother Ġevents Ġin Ġorder Ġto Ġexpress Ġsarc asm . ĠFor Ġexample , Ġthe Ġsentence ĠâĢ ľ If ĠHillary Ġwins , Ġshe Ġwill Ġsurely Ġbe Ġpleased Ġto Ġrecall ĠMonica Ġeach Ġtime Ġshe Ġenters Ġthe ĠOval ĠOffice Ġ: P Ġ: D âĢ Ŀ , Ġwhich Ġrefers Ġto Ġthe Ġ2016 ĠUS Ġpresidential Ġelection Ġcampaign Ġand Ġto Ġthe Ġevents Ġof Ġearly Ġ1990 's Ġrelated Ġto Ġthe ĠUS Ġpresident ĠClinton , Ġis Ġsarcastic Ġbecause ĠHillary , Ġa Ġcandidate Ġand ĠClinton 's Ġwife , Ġwould Ġin Ġfact Ġnot Ġbe Ġpleased Ġto Ġrecall Ġher Ġhusband 's Ġalleged Ġpast Ġaffair Ġwith ĠMonica ĠLew insky . ĠThe Ġsystem , Ġhowever , Ġwould Ġneed Ġa Ġconsiderable Ġamount Ġof Ġfacts , Ġcommons ense Ġknowledge , Ġan aph ora Ġresolution , Ġand Ġlogical Ġreasoning Ġto Ġdraw Ġsuch Ġa Ġconclusion . ĠIn Ġthis Ġpaper , Ġwe Ġwill Ġnot Ġdeal Ġwith Ġsuch Ġcomplex Ġcases . </s> Ex isting Ġworks Ġon Ġsarc asm Ġdetection Ġhave Ġmainly Ġfocused Ġon Ġun ig rams Ġand Ġthe Ġuse Ġof Ġemot icons ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ, Ġun super vised Ġpattern Ġmining Ġapproach ĠB IB REF 4 Ġ, Ġsemi - super vised Ġapproach ĠB IB REF 5 Ġand Ġn - gram s Ġbased Ġapproach ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġwith Ġsentiment Ġfeatures . ĠInstead , Ġwe Ġpropose Ġa Ġframework Ġthat Ġlearns Ġsarc asm Ġfeatures Ġautomatically Ġfrom Ġa Ġsarc asm Ġcorpus Ġusing Ġa Ġconv olution al Ġneural Ġnetwork Ġ( CNN ). ĠWe Ġalso Ġinvestigate Ġwhether Ġfeatures Ġextracted Ġusing Ġthe Ġpre - trained Ġsentiment , Ġemotion Ġand Ġpersonality Ġmodels Ġcan Ġimprove Ġsarc asm Ġdetection Ġperformance . ĠOur Ġapproach Ġuses Ġrelatively Ġlower Ġdimensional Ġfeature Ġvectors Ġand Ġoutper forms Ġthe Ġstate Ġof Ġthe Ġart Ġon Ġdifferent Ġdatasets . ĠIn Ġsummary , Ġthe Ġmain Ġcontributions Ġof Ġthis Ġpaper Ġare Ġthe Ġfollowing : </s> The Ġrest Ġof Ġthe Ġpaper Ġis Ġorganized Ġas Ġfollows : ĠSection ĠSEC REF 2 Ġproposes Ġa Ġbrief Ġliterature Ġreview Ġon Ġsarc asm Ġdetection ; ĠSection ĠSEC REF 4 Ġpresents Ġthe Ġproposed Ġapproach ; Ġexperimental Ġresults Ġand Ġthorough Ġdiscussion Ġon Ġthe Ġexperiments Ġare Ġgiven Ġin ĠSection ĠSEC REF 5 Ġ; Ġfinally , ĠSection ĠSEC REF 6 Ġconcludes Ġthe Ġpaper . </s> Related ĠWorks </s> N LP Ġresearch Ġis Ġgradually Ġevolving Ġfrom Ġlex ical Ġto Ġcompos itional Ġsemantics ĠB IB REF 10 Ġthrough Ġthe Ġadoption Ġof Ġnovel Ġmeaning - pres erving Ġand Ġcontext - aware Ġparad ig ms Ġsuch Ġas Ġconv olution al Ġnetworks ĠB IB REF 11 Ġ, Ġrecurrent Ġbelief Ġnetworks ĠB IB REF 12 Ġ, Ġstatistical Ġlearning Ġtheory ĠB IB REF 13 Ġ, Ġconv olution al Ġmultiple Ġkernel Ġlearning ĠB IB REF 14 Ġ, Ġand Ġcommons ense Ġreasoning ĠB IB REF 15 Ġ. ĠBut Ġwhile Ġother ĠN LP Ġtasks Ġhave Ġbeen Ġextensively Ġinvestigated , Ġsarc asm Ġdetection Ġis Ġa Ġrelatively Ġnew Ġresearch Ġtopic Ġwhich Ġhas Ġgained Ġincreasing Ġinterest Ġonly Ġrecently , Ġpartly Ġthanks Ġto Ġthe Ġrise Ġof Ġsocial Ġmedia Ġanalytics Ġand Ġsentiment Ġanalysis . ĠSent iment Ġanalysis ĠB IB REF 16 Ġand Ġusing Ġmultim od al Ġinformation Ġas Ġa Ġnew Ġtrend ĠB IB REF 17 Ġ, ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ, ĠB IB REF 14 Ġis Ġa Ġpopular Ġbranch Ġof ĠN LP Ġresearch Ġthat Ġaims Ġto Ġunderstand Ġsentiment Ġof Ġdocuments Ġautomatically Ġusing Ġcombination Ġof Ġvarious Ġmachine Ġlearning Ġapproaches ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ, ĠB IB REF 20 Ġ, ĠB IB REF 23 Ġ. </s> An Ġearly Ġwork Ġin Ġthis Ġfield Ġwas Ġdone Ġby ĠB IB REF 6 Ġon Ġa Ġdataset Ġof Ġ6 , 600 Ġmanually Ġannot ated ĠAmazon Ġreviews Ġusing Ġa Ġk NN - class ifier Ġover Ġpunct uation - based Ġand Ġpattern - based Ġfeatures , Ġi . e ., Ġordered Ġsequence Ġof Ġhigh Ġfrequency Ġwords . ĠB IB REF 1 Ġused Ġsupport Ġvector Ġmachine Ġ( S VM ) Ġand Ġlog istic Ġregression Ġover Ġa Ġfeature Ġset Ġof Ġun ig rams , Ġdictionary - based Ġlex ical Ġfeatures Ġand Ġpragmatic Ġfeatures Ġ( e . g ., Ġemot icons ) Ġand Ġcompared Ġthe Ġperformance Ġof Ġthe Ġclass ifier Ġwith Ġthat Ġof Ġhumans . ĠB IB REF 24 Ġdescribed Ġa Ġset Ġof Ġtextual Ġfeatures Ġfor Ġrecognizing Ġirony Ġat Ġa Ġlinguistic Ġlevel , Ġespecially Ġin Ġshort Ġtexts Ġcreated Ġvia ĠTwitter , Ġand Ġconstructed Ġa Ġnew Ġmodel Ġthat Ġwas Ġassessed Ġalong Ġtwo Ġdimensions : Ġrepresent at iveness Ġand Ġrelevance . ĠB IB REF 5 Ġused Ġthe Ġpresence Ġof Ġa Ġpositive Ġsentiment Ġin Ġclose Ġproximity Ġof Ġa Ġnegative Ġsituation Ġphrase Ġas Ġa Ġfeature Ġfor Ġsarc asm Ġdetection . ĠB IB REF 25 Ġused Ġthe ĠBalanced ĠWindow Ġalgorithm Ġfor Ġclass ifying ĠDutch Ġtweets Ġas Ġsarcastic Ġvs . Ġnon - s ar cast ic ; Ġn - gram s Ġ( uni , Ġbi Ġand Ġtri ) Ġand Ġintens ifiers Ġwere Ġused Ġas Ġfeatures Ġfor Ġclassification . </s> BI B REF 26 Ġcompared Ġthe Ġperformance Ġof Ġdifferent Ġclass ifiers Ġon Ġthe ĠAmazon Ġreview Ġdataset Ġusing Ġthe Ġimbalance Ġbetween Ġthe Ġsentiment Ġexpressed Ġby Ġthe Ġreview Ġand Ġthe Ġuser - given Ġstar Ġrating . ĠFeatures Ġbased Ġon Ġfrequency Ġ( gap Ġbetween Ġrare Ġand Ġcommon Ġwords ), Ġwritten Ġspoken Ġgap Ġ( in Ġterms Ġof Ġdifference Ġbetween Ġusage ), Ġsyn onyms Ġ( based Ġon Ġthe Ġdifference Ġin Ġfrequency Ġof Ġsyn onyms ) Ġand Ġambiguity Ġ( number Ġof Ġwords Ġwith Ġmany Ġsyn onyms ) Ġwere Ġused Ġby ĠB IB REF 3 Ġfor Ġsarc asm Ġdetection Ġin Ġtweets . ĠB IB REF 9 Ġproposed Ġthe Ġuse Ġof Ġimplicit Ġinc ong ru ity Ġand Ġexplicit Ġinc ong ru ity Ġbased Ġfeatures Ġalong Ġwith Ġlex ical Ġand Ġpragmatic Ġfeatures , Ġsuch Ġas Ġemot icons Ġand Ġpunct uation Ġmarks . ĠTheir Ġmethod Ġis Ġvery Ġmuch Ġsimilar Ġto Ġthe Ġmethod Ġproposed Ġby ĠB IB REF 5 Ġexcept ĠB IB REF 9 Ġused Ġexplicit Ġinc ong ru ity Ġfeatures . ĠTheir Ġmethod Ġoutper forms Ġthe Ġapproach Ġby ĠB IB REF 5 Ġon Ġtwo Ġdatasets . </s> BI B REF 8 Ġcompared Ġthe Ġperformance Ġwith Ġdifferent Ġlanguage - independent Ġfeatures Ġand Ġpre - processing Ġtechniques Ġfor Ġclass ifying Ġtext Ġas Ġsarcastic Ġand Ġnon - s ar cast ic . ĠThe Ġcomparison Ġwas Ġdone Ġover Ġthree ĠTwitter Ġdataset Ġin Ġtwo Ġdifferent Ġlanguages , Ġtwo Ġof Ġthese Ġin ĠEnglish Ġwith Ġa Ġbalanced Ġand Ġan Ġim balanced Ġdistribution Ġand Ġthe Ġthird Ġone Ġin ĠCzech . ĠThe Ġfeature Ġset Ġincluded Ġn - gram s , Ġword - shape Ġpatterns , Ġpointed ness Ġand Ġpunct uation - based Ġfeatures . </s> In Ġthis Ġwork , Ġwe Ġuse Ġfeatures Ġextracted Ġfrom Ġa Ġdeep ĠCNN Ġfor Ġsarc asm Ġdetection . ĠSome Ġof Ġthe Ġkey Ġdifferences Ġbetween Ġthe Ġproposed Ġapproach Ġand Ġexisting Ġmethods Ġinclude Ġthe Ġuse Ġof Ġa Ġrelatively Ġsmaller Ġfeature Ġset , Ġautomatic Ġfeature Ġextraction , Ġthe Ġuse Ġof Ġdeep Ġnetworks , Ġand Ġthe Ġadoption Ġof Ġpre - trained ĠN LP Ġmodels . </s> Sent iment ĠAnalysis Ġand ĠS arc asm ĠDetection </s> S arc asm Ġdetection Ġis Ġan Ġimportant Ġsubt ask Ġof Ġsentiment Ġanalysis ĠB IB REF 27 Ġ. ĠSince Ġsarcastic Ġsentences Ġare Ġsubjective , Ġthey Ġcarry Ġsentiment Ġand Ġemotion - bearing Ġinformation . ĠMost Ġof Ġthe Ġstudies Ġin Ġthe Ġliterature ĠB IB REF 28 Ġ, ĠB IB REF 29 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 30 Ġinclude Ġsentiment Ġfeatures Ġin Ġsarc asm Ġdetection Ġwith Ġthe Ġuse Ġof Ġa Ġstate - of - the - art Ġsentiment Ġlex icon . ĠBelow , Ġwe Ġexplain Ġhow Ġsentiment Ġinformation Ġis Ġkey Ġto Ġexpress Ġsarcastic Ġopinions Ġand Ġthe Ġapproach Ġwe Ġundertake Ġto Ġexploit Ġsuch Ġinformation Ġfor Ġsarc asm Ġdetection . </s> In Ġgeneral , Ġmost Ġsarcastic Ġsentences Ġcontradict Ġthe Ġfact . ĠIn Ġthe Ġsentence ĠâĢ ľ I Ġlove Ġthe Ġpain Ġpresent Ġin Ġthe Ġbreak ups " Ġ( Figure ĠFIG REF 4 Ġ), Ġfor Ġexample , Ġthe Ġword ĠâĢ ľ love " Ġcontradicts ĠâĢ ľ pain Ġpresent Ġin Ġthe Ġbreak ups âĢ Ŀ , Ġbecause Ġin Ġgeneral Ġno - one Ġloves Ġto Ġbe Ġin Ġpain . ĠIn Ġthis Ġcase , Ġthe Ġfact Ġ( i . e ., ĠâĢ ľ pain Ġin Ġthe Ġbreak ups ") Ġand Ġthe Ġcontradictory Ġstatement Ġto Ġthat Ġfact Ġ( i . e ., ĠâĢ ľ I Ġlove ") Ġexpress Ġsentiment Ġexplicitly . ĠSent iment Ġshifts Ġfrom Ġpositive Ġto Ġnegative Ġbut , Ġaccording Ġto Ġsent ic Ġpatterns ĠB IB REF 31 Ġ, Ġthe Ġliteral Ġsentiment Ġremains Ġpositive . ĠSent ic Ġpatterns , Ġin Ġfact , Ġaim Ġto Ġdetect Ġthe Ġpol arity Ġexpressed Ġby Ġthe Ġspeaker ; Ġthus , Ġwhenever Ġthe Ġconstruction ĠâĢ ľ I Ġlove âĢ Ŀ Ġis Ġencountered , Ġthe Ġsentence Ġis Ġpositive Ġno Ġmatter Ġwhat Ġcomes Ġafter Ġit Ġ( e . g ., ĠâĢ ľ I Ġlove Ġthe Ġmovie Ġthat Ġyou Ġhate âĢ Ŀ ). ĠIn Ġthis Ġcase , Ġhowever , Ġthe Ġsentence Ġcarries Ġsarc asm Ġand , Ġhence , Ġreflects Ġthe Ġnegative Ġsentiment Ġof Ġthe Ġspeaker . </s> In Ġanother Ġexample Ġ( Figure ĠFIG REF 4 Ġ), Ġthe Ġfact , Ġi . e ., ĠâĢ ľ I Ġleft Ġthe Ġtheater Ġduring Ġthe Ġinterval ", Ġhas Ġimplicit Ġnegative Ġsentiment . ĠThe Ġstatement ĠâĢ ľ I Ġlove Ġthe Ġmovie " Ġcontradicts Ġthe Ġfact ĠâĢ ľ I Ġleft Ġthe Ġtheater Ġduring Ġthe Ġinterval "; Ġthus , Ġthe Ġsentence Ġis Ġsarcastic . ĠAlso Ġin Ġthis Ġcase Ġthe Ġsentiment Ġshifts Ġfrom Ġpositive Ġto Ġnegative Ġand Ġhints Ġat Ġthe Ġsarcastic Ġnature Ġof Ġthe Ġopinion . </s> The Ġabove Ġdiscussion Ġhas Ġmade Ġclear Ġthat Ġsentiment Ġ( and , Ġin Ġparticular , Ġsentiment Ġshifts ) Ġcan Ġlargely Ġhelp Ġto Ġdetect Ġsarc asm . ĠIn Ġorder Ġto Ġinclude Ġsentiment Ġshifting Ġinto Ġthe Ġproposed Ġframework , Ġwe Ġtrain Ġa Ġsentiment Ġmodel Ġfor Ġsentiment - specific Ġfeature Ġextraction . ĠTraining Ġwith Ġa ĠCNN Ġhelps Ġto Ġcombine Ġthe Ġlocal Ġfeatures Ġin Ġthe Ġlower Ġlayers Ġinto Ġglobal Ġfeatures Ġin Ġthe Ġhigher Ġlayers . ĠWe Ġdo Ġnot Ġmake Ġuse Ġof Ġsent ic Ġpatterns ĠB IB REF 31 Ġin Ġthis Ġpaper Ġbut Ġwe Ġdo Ġplan Ġto Ġexplore Ġthat Ġresearch Ġdirection Ġas Ġa Ġpart Ġof Ġour Ġfuture Ġwork . ĠIn Ġthe Ġliterature , Ġit Ġis Ġfound Ġthat Ġsarc asm Ġis Ġuser - specific Ġtoo , Ġi . e ., Ġsome Ġusers Ġhave Ġa Ġparticular Ġtendency Ġto Ġpost Ġmore Ġsarcastic Ġtweets Ġthan Ġothers . ĠThis Ġacts Ġas Ġa Ġprimary Ġintuition Ġfor Ġus Ġto Ġextract Ġpersonality - based Ġfeatures Ġfor Ġsarc asm Ġdetection . </s> The ĠProp osed ĠFramework </s> As Ġdiscussed Ġin Ġthe Ġliterature ĠB IB REF 5 Ġ, Ġsarc asm Ġdetection Ġmay Ġdepend Ġon Ġsentiment Ġand Ġother Ġcognitive Ġaspects . ĠFor Ġthis Ġreason , Ġwe Ġincorporate Ġboth Ġsentiment Ġand Ġemotion Ġclues Ġin Ġour Ġframework . ĠAlong Ġwith Ġthese , Ġwe Ġalso Ġargue Ġthat Ġpersonality Ġof Ġthe Ġopinion Ġholder Ġis Ġan Ġimportant Ġfactor Ġfor Ġsarc asm Ġdetection . ĠIn Ġorder Ġto Ġaddress Ġall Ġof Ġthese Ġvariables , Ġwe Ġcreate Ġdifferent Ġmodels Ġfor Ġeach Ġof Ġthem , Ġnamely : Ġsentiment , Ġemotion Ġand Ġpersonality . ĠThe Ġidea Ġis Ġto Ġtrain Ġeach Ġmodel Ġon Ġits Ġcorresponding Ġbenchmark Ġdataset Ġand , Ġhence , Ġuse Ġsuch Ġpre - trained Ġmodels Ġtogether Ġto Ġextract Ġsarc asm - related Ġfeatures Ġfrom Ġthe Ġsarc asm Ġdatasets . </s> Now , Ġthe Ġviable Ġresearch Ġquestion Ġhere Ġis Ġ- ĠDo Ġthese Ġmodels Ġhelp Ġto Ġimprove Ġsarc asm Ġdetection Ġperformance ?' ĠLiterature Ġshows Ġthat Ġthey Ġimprove Ġthe Ġperformance Ġbut Ġnot Ġsignificantly . ĠThus , Ġdo Ġwe Ġneed Ġto Ġconsider Ġthose Ġfactors Ġin Ġspotting Ġsarcastic Ġsentences ? ĠAren 't Ġn - gram s Ġenough Ġfor Ġsarc asm Ġdetection ? ĠThroughout Ġthe Ġrest Ġof Ġthis Ġpaper , Ġwe Ġaddress Ġthese Ġquestions Ġin Ġdetail . ĠThe Ġtraining Ġof Ġeach Ġmodel Ġis Ġdone Ġusing Ġa ĠCNN . ĠBelow , Ġwe Ġexplain Ġthe Ġframework Ġin Ġdetail . ĠThen , Ġwe Ġdiscuss Ġthe Ġpre - trained Ġmodels . ĠFigure ĠFIG REF 6 Ġpresents Ġa Ġvisualization Ġof Ġthe Ġproposed Ġframework . </s> General ĠCNN ĠFramework </s> CNN Ġcan Ġautomatically Ġextract Ġkey Ġfeatures Ġfrom Ġthe Ġtraining Ġdata . ĠIt Ġgr as ps Ġcontextual Ġlocal Ġfeatures Ġfrom Ġa Ġsentence Ġand , Ġafter Ġseveral Ġconv olution Ġoperations , Ġit Ġforms Ġa Ġglobal Ġfeature Ġvector Ġout Ġof Ġthose Ġlocal Ġfeatures . ĠCNN Ġdoes Ġnot Ġneed Ġthe Ġhand - crafted Ġfeatures Ġused Ġin Ġtraditional Ġsupervised Ġclass ifiers . ĠSuch Ġhand - crafted Ġfeatures Ġare Ġdifficult Ġto Ġcompute Ġand Ġa Ġgood Ġguess Ġfor Ġencoding Ġthe Ġfeatures Ġis Ġalways Ġnecessary Ġin Ġorder Ġto Ġget Ġsatisfactory Ġresults . ĠCNN , Ġinstead , Ġuses Ġa Ġhierarchy Ġof Ġlocal Ġfeatures Ġwhich Ġare Ġimportant Ġto Ġlearn Ġcontext . ĠThe Ġhand - crafted Ġfeatures Ġoften Ġignore Ġsuch Ġa Ġhierarchy Ġof Ġlocal Ġfeatures . </s> Features Ġextracted Ġby ĠCNN Ġcan Ġtherefore Ġbe Ġused Ġinstead Ġof Ġhand - crafted Ġfeatures , Ġas Ġthey Ġcarry Ġmore Ġuseful Ġinformation . ĠThe Ġidea Ġbehind Ġconv olution Ġis Ġto Ġtake Ġthe Ġdot Ġproduct Ġof Ġa Ġvector Ġof ĠIN LINE FORM 0 Ġweights ĠIN LINE FORM 1 Ġalso Ġknown Ġas Ġkernel Ġvector Ġwith Ġeach ĠIN LINE FORM 2 Ġ- gram Ġin Ġthe Ġsentence ĠIN LINE FORM 3 Ġto Ġobtain Ġanother Ġsequence Ġof Ġfeatures ĠIN LINE FORM 4 Ġ. ĠDIS PLAY FORM 0 </s> Thus , Ġa Ġmax Ġpool ing Ġoperation Ġis Ġapplied Ġover Ġthe Ġfeature Ġmap Ġand Ġthe Ġmaximum Ġvalue ĠIN LINE FORM 0 Ġis Ġtaken Ġas Ġthe Ġfeature Ġcorresponding Ġto Ġthis Ġparticular Ġkernel Ġvector . ĠSimilarly , Ġvarying Ġkernel Ġvectors Ġand Ġwindow Ġsizes Ġare Ġused Ġto Ġobtain Ġmultiple Ġfeatures ĠB IB REF 32 Ġ. ĠFor Ġeach Ġword ĠIN LINE FORM 1 Ġin Ġthe Ġvocabulary , Ġa ĠIN LINE FORM 2 Ġ- dimensional Ġvector Ġrepresentation Ġis Ġgiven Ġin Ġa Ġlook Ġup Ġtable Ġthat Ġis Ġlearned Ġfrom Ġthe Ġdata ĠB IB REF 33 Ġ. ĠThe Ġvector Ġrepresentation Ġof Ġa Ġsentence , Ġhence , Ġis Ġa Ġconc aten ation Ġof Ġvectors Ġfor Ġindividual Ġwords . ĠSimilarly , Ġwe Ġcan Ġhave Ġlook Ġup Ġtables Ġfor Ġother Ġfeatures . ĠOne Ġmight Ġwant Ġto Ġprovide Ġfeatures Ġother Ġthan Ġwords Ġif Ġthese Ġfeatures Ġare Ġsuspected Ġto Ġbe Ġhelpful . ĠThe Ġconv olution Ġkernels Ġare Ġthen Ġapplied Ġto Ġword Ġvectors Ġinstead Ġof Ġindividual Ġwords . </s> We Ġuse Ġthese Ġfeatures Ġto Ġtrain Ġhigher Ġlayers Ġof Ġthe ĠCNN , Ġin Ġorder Ġto Ġrepresent Ġbigger Ġgroups Ġof Ġwords Ġin Ġsentences . ĠWe Ġdenote Ġthe Ġfeature Ġlearned Ġat Ġhidden Ġneuron ĠIN LINE FORM 0 Ġin Ġlayer ĠIN LINE FORM 1 Ġas ĠIN LINE FORM 2 Ġ. ĠMultiple Ġfeatures Ġmay Ġbe Ġlearned Ġin Ġparallel Ġin Ġthe Ġsame ĠCNN Ġlayer . ĠThe Ġfeatures Ġlearned Ġin Ġeach Ġlayer Ġare Ġused Ġto Ġtrain Ġthe Ġnext Ġlayer : ĠDIS PLAY FORM 0 </s> where Ġ* Ġindicates Ġconv olution Ġand ĠIN LINE FORM 0 Ġis Ġa Ġweight Ġkernel Ġfor Ġhidden Ġneuron ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġis Ġthe Ġtotal Ġnumber Ġof Ġhidden Ġneurons . ĠThe ĠCNN Ġsentence Ġmodel Ġpreserves Ġthe Ġorder Ġof Ġwords Ġby Ġadopting Ġconv olution Ġkernels Ġof Ġgradually Ġincreasing Ġsizes Ġthat Ġspan Ġan Ġincreasing Ġnumber Ġof Ġwords Ġand Ġultimately Ġthe Ġentire Ġsentence . ĠAs Ġmentioned Ġabove , Ġeach Ġword Ġin Ġa Ġsentence Ġis Ġrepresented Ġusing Ġword Ġembed d ings . </s> We Ġemploy Ġthe Ġpublicly Ġavailable Ġword 2 vec Ġvectors , Ġwhich Ġwere Ġtrained Ġon Ġ100 Ġbillion Ġwords Ġfrom ĠGoogle ĠNews . ĠThe Ġvectors Ġare Ġof Ġdimension ality Ġ300 , Ġtrained Ġusing Ġthe Ġcontinuous Ġbag - of - words Ġarchitecture ĠB IB REF 33 Ġ. ĠWords Ġnot Ġpresent Ġin Ġthe Ġset Ġof Ġpre - trained Ġwords Ġare Ġinitialized Ġrandomly . ĠHowever , Ġwhile Ġtraining Ġthe Ġneural Ġnetwork , Ġwe Ġuse Ġnon - static Ġrepresentations . ĠThese Ġinclude Ġthe Ġword Ġvectors , Ġtaken Ġas Ġinput , Ġinto Ġthe Ġlist Ġof Ġparameters Ġto Ġbe Ġlearned Ġduring Ġtraining . </s> Two Ġprimary Ġreasons Ġmotivated Ġus Ġto Ġuse Ġnon - static Ġchannels Ġas Ġopposed Ġto Ġstatic Ġones . ĠFirstly , Ġthe Ġcommon Ġpresence Ġof Ġinformal Ġlanguage Ġand Ġwords Ġin Ġtweets Ġresulted Ġin Ġa Ġrelatively Ġhigh Ġrandom Ġinitialization Ġof Ġword Ġvectors Ġdue Ġto Ġthe Ġun availability Ġof Ġthese Ġwords Ġin Ġthe Ġword 2 vec Ġdictionary . ĠSecondly , Ġsarcastic Ġsentences Ġare Ġknown Ġto Ġinclude Ġpol arity Ġshifts Ġin Ġsentimental Ġand Ġemotional Ġdegrees . ĠFor Ġexample , ĠâĢ ľ I Ġlove Ġthe Ġpain Ġpresent Ġin Ġbreak ups " Ġis Ġa Ġsarcastic Ġsentence Ġwith Ġa Ġsignificant Ġchange Ġin Ġsentimental Ġpol arity . ĠAs Ġword 2 vec Ġwas Ġnot Ġtrained Ġto Ġincorporate Ġthese Ġnuances , Ġwe Ġallow Ġour Ġmodels Ġto Ġupdate Ġthe Ġembed d ings Ġduring Ġtraining Ġin Ġorder Ġto Ġinclude Ġthem . ĠEach Ġsentence Ġis Ġwrapped Ġto Ġa Ġwindow Ġof ĠIN LINE FORM 0 Ġ, Ġwhere ĠIN LINE FORM 1 Ġis Ġthe Ġmaximum Ġnumber Ġof Ġwords Ġamongst Ġall Ġsentences Ġin Ġthe Ġdataset . ĠWe Ġuse Ġthe Ġoutput Ġof Ġthe Ġfully - connected Ġlayer Ġof Ġthe Ġnetwork Ġas Ġour Ġfeature Ġvector . </s> We Ġhave Ġdone Ġtwo Ġkinds Ġof Ġexperiments : Ġfirst ly , Ġwe Ġused ĠCNN Ġfor Ġthe Ġclassification ; Ġsecondly , Ġwe Ġextracted Ġfeatures Ġfrom Ġthe Ġfully - connected Ġlayer Ġof Ġthe ĠCNN Ġand Ġfed Ġthem Ġto Ġan ĠS VM Ġfor Ġthe Ġfinal Ġclassification . ĠThe Ġlatter ĠCNN - S VM Ġscheme Ġis Ġquite Ġuseful Ġfor Ġtext Ġclassification Ġas Ġshown Ġby ĠP oria Ġet Ġal . ĠB IB REF 18 Ġ. ĠWe Ġcarry Ġout Ġn - fold Ġcross - valid ation Ġon Ġthe Ġdataset Ġusing ĠCNN . ĠIn Ġevery Ġfold Ġiteration , Ġin Ġorder Ġto Ġobtain Ġthe Ġtraining Ġand Ġtest Ġfeatures , Ġthe Ġoutput Ġof Ġthe Ġfully - connected Ġlayer Ġis Ġtreated Ġas Ġfeatures Ġto Ġbe Ġused Ġfor Ġthe Ġfinal Ġclassification Ġusing ĠS VM . ĠTable ĠT AB REF 12 Ġshows Ġthe Ġtraining Ġsettings Ġfor Ġeach ĠCNN Ġmodel Ġdeveloped Ġin Ġthis Ġwork . ĠRe LU Ġis Ġused Ġas Ġthe Ġnon - linear Ġactivation Ġfunction Ġof Ġthe Ġnetwork . ĠThe Ġnetwork Ġconfigurations Ġof Ġall Ġmodels Ġdeveloped Ġin Ġthis Ġwork Ġare Ġgiven Ġin ĠTable ĠT AB REF 12 Ġ. </s> Sent iment ĠFeature ĠExt raction ĠModel </s> As Ġdiscussed Ġabove , Ġsentiment Ġclues Ġplay Ġan Ġimportant Ġrole Ġfor Ġsarcastic Ġsentence Ġdetection . ĠIn Ġour Ġwork , Ġwe Ġtrain Ġa ĠCNN Ġ( see ĠSection ĠSEC REF 5 Ġfor Ġdetails ) Ġon Ġa Ġsentiment Ġbenchmark Ġdataset . ĠThis Ġpre - trained Ġmodel Ġis Ġthen Ġused Ġto Ġextract Ġfeatures Ġfrom Ġthe Ġsarcastic Ġdatasets . ĠIn Ġparticular , Ġwe Ġuse ĠSem eval Ġ2014 ĠB IB REF 34 ĠTwitter ĠSent iment ĠAnalysis ĠDat as et Ġfor Ġthe Ġtraining . ĠThis Ġdataset Ġcontains Ġ9 , 497 Ġtweets Ġout Ġof Ġwhich Ġ5 , 8 95 Ġare Ġpositive , Ġ3 , 131 Ġare Ġnegative Ġand Ġ4 71 Ġare Ġneutral . ĠThe Ġfully - connected Ġlayer Ġof Ġthe ĠCNN Ġused Ġfor Ġsentiment Ġfeature Ġextraction Ġhas Ġ100 Ġneurons , Ġso Ġ100 Ġfeatures Ġare Ġextracted Ġfrom Ġthis Ġpre - trained Ġmodel . ĠThe Ġfinal Ġsoft max Ġdetermines Ġwhether Ġa Ġsentence Ġis Ġpositive , Ġnegative Ġor Ġneutral . ĠThus , Ġwe Ġhave Ġthree Ġneurons Ġin Ġthe Ġsoft max Ġlayer . </s> Em otion ĠFeature ĠExt raction ĠModel </s> We Ġuse Ġthe ĠCNN Ġstructure Ġas Ġdescribed Ġin ĠSection ĠSEC REF 5 Ġfor Ġemotional Ġfeature Ġextraction . ĠAs Ġa Ġdataset Ġfor Ġextracting Ġemotion - related Ġfeatures , Ġwe Ġuse Ġthe Ġcorpus Ġdeveloped Ġby ĠB IB REF 35 Ġ. ĠThis Ġdataset Ġconsists Ġof Ġblog Ġposts Ġlabeled Ġby Ġtheir Ġcorresponding Ġemotion Ġcategories . ĠAs Ġemotion Ġtax onomy , Ġthe Ġauthors Ġused Ġsix Ġbasic Ġemotions , Ġi . e ., ĠAnger , ĠDis g ust , ĠSurprise , ĠSad ness , ĠJoy Ġand ĠFear . ĠIn Ġparticular , Ġthe Ġblog Ġposts Ġwere Ġsplit Ġinto Ġsentences Ġand Ġeach Ġsentence Ġwas Ġlabeled . ĠThe Ġdataset Ġcontains Ġ5 , 205 Ġsentences Ġlabeled Ġby Ġone Ġof Ġthe Ġemotion Ġlabels . ĠAfter Ġemploying Ġthis Ġmodel Ġon Ġthe Ġsarc asm Ġdataset , Ġwe Ġobtained Ġa Ġ150 - dimensional Ġfeature Ġvector Ġfrom Ġthe Ġfully - connected Ġlayer . ĠAs Ġthe Ġaim Ġof Ġtraining Ġis Ġto Ġclassify Ġeach Ġsentence Ġinto Ġone Ġof Ġthe Ġsix Ġemotion Ġclasses , Ġwe Ġused Ġsix Ġneurons Ġin Ġthe Ġsoft max Ġlayer . </s> Person ality ĠFeature ĠExt raction ĠModel </s> Detect ing Ġpersonality Ġfrom Ġtext Ġis Ġa Ġwell - known Ġchallenging Ġproblem . ĠIn Ġour Ġwork , Ġwe Ġuse Ġfive Ġpersonality Ġtraits Ġdescribed Ġby ĠB IB REF 36 Ġ, Ġi . e ., ĠOpen ness , ĠCons cientious ness , ĠExtra version , ĠAg ree abl eness , Ġand ĠNeuro t icism , Ġsometimes Ġabbrevi ated Ġas ĠO CE AN Ġ( by Ġtheir Ġfirst Ġletters ). ĠAs Ġa Ġtraining Ġdataset , Ġwe Ġuse Ġthe Ġcorpus Ġdeveloped Ġby ĠB IB REF 36 Ġ, Ġwhich Ġcontains Ġ2 , 400 Ġessays Ġlabeled Ġby Ġone Ġof Ġthe Ġfive Ġpersonality Ġtraits Ġeach . </s> The Ġfully - connected Ġlayer Ġhas Ġ150 Ġneurons , Ġwhich Ġare Ġtreated Ġas Ġthe Ġfeatures . ĠWe Ġconc aten ate Ġthe Ġfeature Ġvector Ġof Ġeach Ġpersonality Ġdimension Ġin Ġorder Ġto Ġcreate Ġthe Ġfinal Ġfeature Ġvector . ĠThus , Ġthe Ġpersonality Ġmodel Ġultimately Ġextracts Ġa Ġ750 - dimensional Ġfeature Ġvector Ġ( 150 - dimensional Ġfeature Ġvector Ġfor Ġeach Ġof Ġthe Ġfive Ġpersonality Ġtraits ). ĠThis Ġnetwork Ġis Ġreplicated Ġfive Ġtimes , Ġone Ġfor Ġeach Ġpersonality Ġtrait . ĠIn Ġparticular , Ġwe Ġcreate Ġa ĠCNN Ġfor Ġeach Ġpersonality Ġtrait Ġand Ġthe Ġaim Ġof Ġeach ĠCNN Ġis Ġto Ġclassify Ġa Ġsentence Ġinto Ġbinary Ġclasses , Ġi . e ., Ġwhether Ġit Ġexpresses Ġa Ġpersonality Ġtrait Ġor Ġnot . </s> Bas eline ĠMethod Ġand ĠFeatures </s> CNN Ġcan Ġalso Ġbe Ġemployed Ġon Ġthe Ġsarc asm Ġdatasets Ġin Ġorder Ġto Ġidentify Ġsarcastic Ġand Ġnon - s ar cast ic Ġtweets . ĠWe Ġterm Ġthe Ġfeatures Ġextracted Ġfrom Ġthis Ġnetwork Ġbaseline Ġfeatures , Ġthe Ġmethod Ġas Ġbaseline Ġmethod Ġand Ġthe ĠCNN Ġarchitecture Ġused Ġin Ġthis Ġbaseline Ġmethod Ġas Ġbaseline ĠCNN . ĠSince Ġthe Ġfully - connected Ġlayer Ġhas Ġ100 Ġneurons , Ġwe Ġhave Ġ100 Ġbaseline Ġfeatures Ġin Ġour Ġexperiment . ĠThis Ġmethod Ġis Ġtermed Ġbaseline Ġmethod Ġas Ġit Ġdirectly Ġaims Ġto Ġclassify Ġa Ġsentence Ġas Ġsarcastic Ġvs Ġnon - s ar cast ic . ĠThe Ġbaseline ĠCNN Ġextracts Ġthe Ġinherent Ġsemantics Ġfrom Ġthe Ġsarcastic Ġcorpus Ġby Ġemploying Ġdeep Ġdomain Ġunderstanding . ĠThe Ġprocess Ġof Ġusing Ġbaseline Ġfeatures Ġwith Ġother Ġfeatures Ġextracted Ġfrom Ġthe Ġpre - trained Ġmodel Ġis Ġdescribed Ġin ĠSection ĠSEC REF 24 Ġ. </s> Exper imental ĠResults Ġand ĠDiscussion </s> In Ġthis Ġsection , Ġwe Ġpresent Ġthe Ġexperimental Ġresults Ġusing Ġdifferent Ġfeature Ġcombinations Ġand Ġcompare Ġthem Ġwith Ġthe Ġstate Ġof Ġthe Ġart . ĠFor Ġeach Ġfeature Ġwe Ġshow Ġthe Ġresults Ġusing Ġonly ĠCNN Ġand Ġusing ĠCNN - S VM Ġ( i . e ., Ġwhen Ġthe Ġfeatures Ġextracted Ġby ĠCNN Ġare Ġfed Ġto Ġthe ĠS VM ). ĠMacro - F 1 Ġmeasure Ġis Ġused Ġas Ġan Ġevaluation Ġscheme Ġin Ġthe Ġexperiments . </s> S arc asm ĠDat as ets ĠUsed Ġin Ġthe ĠExperiment </s> This Ġdataset Ġwas Ġcreated Ġby ĠB IB REF 8 Ġ. ĠThe Ġtweets Ġwere Ġdownloaded Ġfrom ĠTwitter Ġusing Ġ# s arc asm Ġas Ġa Ġmarker Ġfor Ġsarcastic Ġtweets . ĠIt Ġis Ġa Ġmon oling ual ĠEnglish Ġdataset Ġwhich Ġconsists Ġof Ġa Ġbalanced Ġdistribution Ġof Ġ50 , 000 Ġsarcastic Ġtweets Ġand Ġ50 , 000 Ġnon - s ar cast ic Ġtweets . </s> Since Ġsarcastic Ġtweets Ġare Ġless Ġfrequently Ġused ĠB IB REF 8 Ġ, Ġwe Ġalso Ġneed Ġto Ġinvestigate Ġthe Ġrobust ness Ġof Ġthe Ġselected Ġfeatures Ġand Ġthe Ġmodel Ġtrained Ġon Ġthese Ġfeatures Ġon Ġan Ġim balanced Ġdataset . ĠTo Ġthis Ġend , Ġwe Ġused Ġanother ĠEnglish Ġdataset Ġfrom ĠB IB REF 8 Ġ. ĠIt Ġconsists Ġof Ġ25 , 000 Ġsarcastic Ġtweets Ġand Ġ75 , 000 Ġnon - s ar cast ic Ġtweets . </s> We Ġhave Ġobtained Ġthis Ġdataset Ġfrom ĠThe ĠS arc asm ĠDet ector . ĠIt Ġcontains Ġ120 , 000 Ġtweets , Ġout Ġof Ġwhich Ġ20 , 000 Ġare Ġsarcastic Ġand Ġ100 , 000 Ġare Ġnon - s ar cast ic . ĠWe Ġrandomly Ġsampled Ġ10 , 000 Ġsarcastic Ġand Ġ20 , 000 Ġnon - s ar cast ic Ġtweets Ġfrom Ġthe Ġdataset . ĠVisual ization Ġof Ġboth Ġthe Ġoriginal Ġand Ġsubset Ġdata Ġshow Ġsimilar Ġcharacteristics . </s> A Ġtwo - step Ġmethodology Ġhas Ġbeen Ġemployed Ġin Ġfiltering Ġthe Ġdatasets Ġused Ġin Ġour Ġexperiments . ĠFirstly , Ġwe Ġidentified Ġand Ġremoved Ġall Ġthe ĠâĢ ľ user ", ĠâĢ ľ URL " Ġand ĠâĢ ľ hash tag " Ġreferences Ġpresent Ġin Ġthe Ġtweets Ġusing Ġefficient Ġregular Ġexpressions . ĠSpecial Ġemphasis Ġwas Ġgiven Ġto Ġthis Ġstep Ġto Ġavoid Ġtraces Ġof Ġhasht ags , Ġwhich Ġmight Ġtrigger Ġthe Ġmodels Ġto Ġprovide Ġbiased Ġresults . ĠSecondly , Ġwe Ġused ĠNL T K ĠTwitter ĠToken izer Ġto Ġensure Ġproper Ġtoken ization Ġof Ġwords Ġalong Ġwith Ġspecial Ġsymbols Ġand Ġemot icons . ĠSince Ġour Ġdeep ĠCNN s Ġextract Ġcontextual Ġinformation Ġpresent Ġin Ġtweets , Ġwe Ġinclude Ġemot icons Ġas Ġpart Ġof Ġthe Ġvocabulary . ĠThis Ġenables Ġthe Ġemot icons Ġto Ġhold Ġa Ġplace Ġin Ġthe Ġword Ġembed ding Ġspace Ġand Ġaid Ġin Ġproviding Ġinformation Ġabout Ġthe Ġemotions Ġpresent Ġin Ġthe Ġsentence . </s> Mer ging Ġthe ĠFeatures </s> Throughout Ġthis Ġresearch , Ġwe Ġhave Ġcarried Ġout Ġseveral Ġexperiments Ġwith Ġvarious Ġfeature Ġcombinations . ĠFor Ġthe Ġsake Ġof Ġclarity , Ġwe Ġexplain Ġbelow Ġhow Ġthe Ġfeatures Ġextracted Ġusing Ġdifference Ġmodels Ġare Ġmerged . </s> In Ġthe Ġstandard Ġfeature Ġmerging Ġprocess , Ġwe Ġfirst Ġextract Ġthe Ġfeatures Ġfrom Ġall Ġdeep ĠCNN Ġbased Ġfeature Ġextraction Ġmodels Ġand Ġthen Ġwe Ġconc aten ate Ġthem . ĠAfterwards , ĠS VM Ġis Ġemployed Ġon Ġthe Ġresulted Ġfeature Ġvector . </s> In Ġanother Ġsetting , Ġwe Ġuse Ġthe Ġfeatures Ġextracted Ġfrom Ġthe Ġpre - trained Ġmodels Ġas Ġthe Ġstatic Ġchannels Ġof Ġfeatures Ġin Ġthe ĠCNN Ġof Ġthe Ġbaseline Ġmethod . ĠThese Ġfeatures Ġare Ġapp ended Ġto Ġthe Ġhidden Ġlayer Ġof Ġthe Ġbaseline ĠCNN , Ġpreceding Ġthe Ġfinal Ġoutput Ġsoft max Ġlayer . </s> For Ġcomparison , Ġwe Ġhave Ġre - im ple mented Ġthe Ġstate - of - the - art Ġmethods . ĠSince ĠB IB REF 9 Ġdid Ġnot Ġmention Ġabout Ġthe Ġsentiment Ġlex icon Ġthey Ġuse Ġin Ġthe Ġexperiment , Ġwe Ġused ĠSent ic Net ĠB IB REF 37 Ġin Ġthe Ġre - im plementation Ġof Ġtheir Ġmethod . </s> Results Ġon ĠDat as et Ġ1 </s> As Ġshown Ġin ĠTable ĠT AB REF 29 Ġ, Ġfor Ġevery Ġfeature ĠCNN - S VM Ġoutper forms Ġthe Ġperformance Ġof Ġthe ĠCNN . ĠFollowing ĠB IB REF 6 Ġ, Ġwe Ġhave Ġcarried Ġout Ġa Ġ5 - fold Ġcross - valid ation Ġon Ġthis Ġdataset . ĠThe Ġbaseline Ġfeatures Ġ( ĠSEC REF 16 Ġ) Ġperform Ġbest Ġamong Ġother Ġfeatures . ĠAmong Ġall Ġthe Ġpre - trained Ġmodels , Ġthe Ġsentiment Ġmodel Ġ( F 1 - score : Ġ87 . 00 %) Ġachieves Ġbetter Ġperformance Ġin Ġcomparison Ġwith Ġthe Ġother Ġtwo Ġpre - trained Ġmodels . ĠInterestingly , Ġwhen Ġwe Ġmerge Ġthe Ġbaseline Ġfeatures Ġwith Ġthe Ġfeatures Ġextracted Ġby Ġthe Ġpre - trained Ġdeep ĠN LP Ġmodels , Ġwe Ġonly Ġget Ġ0 . 11 % Ġimprovement Ġover Ġthe ĠF - score . ĠIt Ġmeans Ġthat Ġthe Ġbaseline Ġfeatures Ġalone Ġare Ġquite Ġcapable Ġto Ġdetect Ġsarc asm . ĠOn Ġthe Ġother Ġhand , Ġwhen Ġwe Ġcombine Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures , Ġwe Ġobtain Ġ90 . 70 % ĠF 1 - score . ĠThis Ġindicates Ġthat Ġthe Ġpre - trained Ġfeatures Ġare Ġindeed Ġuseful Ġfor Ġsarc asm Ġdetection . ĠWe Ġalso Ġcompare Ġour Ġapproach Ġwith Ġthe Ġbest Ġresearch Ġstudy Ġconducted Ġon Ġthis Ġdataset Ġ( Table ĠT AB REF 30 Ġ). ĠBoth Ġthe Ġproposed Ġbaseline Ġmodel Ġand Ġthe Ġbaseline Ġ+ Ġsentiment Ġ+ Ġemotion Ġ+ Ġpersonality Ġmodel Ġoutper form Ġthe Ġstate Ġof Ġthe Ġart ĠB IB REF 9 Ġ, ĠB IB REF 8 Ġ. ĠOne Ġimportant Ġdifference Ġwith Ġthe Ġstate Ġof Ġthe Ġart Ġis Ġthat ĠB IB REF 8 Ġused Ġrelatively Ġlarger Ġfeature Ġvector Ġsize Ġ( ĠIN LINE FORM 0 Ġ500 , 000 ) Ġthan Ġwe Ġused Ġin Ġour Ġexperiment Ġ( 1 , 100 ). ĠThis Ġnot Ġonly Ġprevents Ġour Ġmodel Ġto Ġover fit Ġthe Ġdata Ġbut Ġalso Ġspeeds Ġup Ġthe Ġcomputation . ĠThus , Ġwe Ġobtain Ġan Ġimprovement Ġin Ġthe Ġoverall Ġperformance Ġwith Ġautomatic Ġfeature Ġextraction Ġusing Ġa Ġrelatively Ġlower Ġdimensional Ġfeature Ġspace . </s> In Ġthe Ġliterature , Ġword Ġn - gram s , Ġskip gram s Ġand Ġcharacter Ġn - gram s Ġare Ġused Ġas Ġbaseline Ġfeatures . ĠAccording Ġto ĠPt ace k Ġet Ġal . ĠB IB REF 8 Ġ, Ġthese Ġbaseline Ġfeatures Ġalong Ġwith Ġthe Ġother Ġfeatures Ġ( sent iment Ġfeatures Ġand Ġpart - of - speech Ġbased Ġfeatures ) Ġproduced Ġthe Ġbest Ġperformance . ĠHowever , ĠPt ace k Ġet Ġal . Ġdid Ġnot Ġanalyze Ġthe Ġperformance Ġof Ġthese Ġfeatures Ġwhen Ġthey Ġwere Ġnot Ġused Ġwith Ġthe Ġbaseline Ġfeatures . ĠPre - trained Ġword Ġembed d ings Ġplay Ġan Ġimportant Ġrole Ġin Ġthe Ġperformance Ġof Ġthe Ġclass ifier Ġbecause , Ġwhen Ġwe Ġuse Ġrandomly Ġgenerated Ġembed d ings , Ġperformance Ġfalls Ġdown Ġto Ġ86 . 23 % Ġusing Ġall Ġfeatures . </s> Results Ġon ĠDat as et Ġ2 </s> 5 - fold Ġcross - valid ation Ġhas Ġbeen Ġcarried Ġout Ġon ĠDat as et Ġ2 . ĠAlso Ġfor Ġthis Ġdataset , Ġwe Ġget Ġthe Ġbest Ġaccuracy Ġwhen Ġwe Ġuse Ġall Ġfeatures . ĠBas eline Ġfeatures Ġhave Ġperformed Ġsignificantly Ġbetter Ġ( F 1 - score : Ġ92 . 32 %) Ġthan Ġall Ġother Ġfeatures . ĠSupporting Ġthe Ġobservations Ġwe Ġhave Ġmade Ġfrom Ġthe Ġexperiments Ġon ĠDat as et Ġ1 , Ġwe Ġsee ĠCNN - S VM Ġoutper forming ĠCNN Ġon ĠDat as et Ġ2 . ĠHowever , Ġwhen Ġwe Ġuse Ġall Ġthe Ġfeatures , ĠCNN Ġalone Ġ( F 1 - score : Ġ89 . 73 %) Ġdoes Ġnot Ġoutper form Ġthe Ġstate Ġof Ġthe Ġart ĠB IB REF 8 Ġ( F 1 - score : Ġ92 . 37 %). ĠAs Ġshown Ġin ĠTable ĠT AB REF 30 Ġ, ĠCNN - S VM Ġon Ġthe Ġbaseline Ġ+ Ġsentiment Ġ+ Ġemotion Ġ+ Ġpersonality Ġfeature Ġset Ġoutper forms Ġthe Ġstate Ġof Ġthe Ġart Ġ( F 1 - score : Ġ94 . 80 %). ĠAmong Ġthe Ġpre - trained Ġmodels , Ġthe Ġsentiment Ġmodel Ġperforms Ġbest Ġ( F 1 - score : Ġ87 . 00 %). </s> Table ĠT AB REF 29 Ġshows Ġthe Ġperformance Ġof Ġdifferent Ġfeature Ġcombinations . ĠThe Ġgap Ġbetween Ġthe ĠF 1 - sc ores Ġof Ġonly Ġbaseline Ġfeatures Ġand Ġall Ġfeatures Ġis Ġlarger Ġon Ġthe Ġim balanced Ġdataset Ġthan Ġthe Ġbalanced Ġdataset . ĠThis Ġsupports Ġour Ġclaim Ġthat Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures Ġare Ġvery Ġuseful Ġfor Ġsarc asm Ġdetection , Ġthanks Ġto Ġthe Ġpre - trained Ġmodels . ĠThe ĠF 1 - score Ġusing Ġsentiment Ġfeatures Ġwhen Ġcombined Ġwith Ġbaseline Ġfeatures Ġis Ġ94 . 60 %. ĠOn Ġboth Ġof Ġthe Ġdatasets , Ġemotion Ġand Ġsentiment Ġfeatures Ġperform Ġbetter Ġthan Ġthe Ġpersonality Ġfeatures . ĠInterestingly , Ġusing Ġonly Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures , Ġwe Ġachieve Ġ90 . 90 % ĠF 1 - score . </s> Results Ġon ĠDat as et Ġ3 </s> Exper imental Ġresults Ġon ĠDat as et Ġ3 Ġshow Ġthe Ġsimilar Ġtrends Ġ( Table ĠT AB REF 30 Ġ) Ġas Ġcompared Ġto ĠDat as et Ġ1 Ġand ĠDat as et Ġ2 . ĠThe Ġhighest Ġperformance Ġ( F 1 - score Ġ93 . 30 %) Ġis Ġobtained Ġwhen Ġwe Ġcombine Ġbaseline Ġfeatures Ġwith Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures . ĠIn Ġthis Ġcase , Ġalso ĠCNN - S VM Ġconsistently Ġperforms Ġbetter Ġthan ĠCNN Ġfor Ġevery Ġfeature Ġcombination . ĠThe Ġsentiment Ġmodel Ġis Ġfound Ġto Ġbe Ġthe Ġbest Ġpre - trained Ġmodel . ĠF 1 - score Ġof Ġ84 . 43 % Ġis Ġobtained Ġwhen Ġwe Ġmerge Ġsentiment , Ġemotion Ġand Ġpersonality Ġfeatures . </s> Dat as et Ġ3 Ġis Ġmore Ġcomplex Ġand Ġnon - linear Ġin Ġnature Ġcompared Ġto Ġthe Ġother Ġtwo Ġdatasets . ĠAs Ġshown Ġin ĠTable ĠT AB REF 30 Ġ, Ġthe Ġmethods Ġby ĠB IB REF 9 Ġand ĠB IB REF 8 Ġperform Ġpoorly Ġon Ġthis Ġdataset . ĠThe ĠTP Ġrate Ġachieved Ġby ĠB IB REF 9 Ġis Ġonly Ġ10 . 07 % Ġand Ġthat Ġmeans Ġtheir Ġmethod Ġsuffers Ġbadly Ġon Ġcomplex Ġdata . ĠThe Ġapproach Ġof ĠB IB REF 8 Ġhas Ġalso Ġfailed Ġto Ġperform Ġwell Ġon ĠDat as et Ġ3 , Ġachieving Ġ62 . 37 % Ġwith Ġa Ġbetter ĠTP Ġrate Ġof Ġ22 . 15 % Ġthan ĠB IB REF 9 Ġ. ĠOn Ġthe Ġother Ġhand , Ġour Ġproposed Ġmodel Ġperforms Ġconsistently Ġwell Ġon Ġthis Ġdataset Ġachieving Ġ93 . 30 %. </s> Testing ĠGeneral iz ability Ġof Ġthe ĠModels Ġand ĠDisc ussions </s> To Ġtest Ġthe Ġgeneral ization Ġcapability Ġof Ġthe Ġproposed Ġapproach , Ġwe Ġperform Ġtraining Ġon ĠDat as et Ġ1 Ġand Ġtest Ġon ĠDat as et Ġ3 . ĠThe ĠF 1 - score Ġdrops Ġdown Ġdramatically Ġto Ġ33 . 05 %. ĠIn Ġorder Ġto Ġunderstand Ġthis Ġfinding , Ġwe Ġvisualize Ġeach Ġdataset Ġusing ĠPC A Ġ( Figure ĠFIG REF 17 Ġ). ĠIt Ġdepicts Ġthat , Ġalthough ĠDat as et Ġ1 Ġis Ġmostly Ġlin early Ġsepar able , ĠDat as et Ġ3 Ġis Ġnot . ĠA Ġlinear Ġkernel Ġthat Ġperforms Ġwell Ġon ĠDat as et Ġ1 Ġfails Ġto Ġprovide Ġgood Ġperformance Ġon ĠDat as et Ġ3 . ĠIf Ġwe Ġuse ĠRB F Ġkernel , Ġit Ġover fits Ġthe Ġdata Ġand Ġproduces Ġworse Ġresults Ġthan Ġwhat Ġwe Ġget Ġusing Ġlinear Ġkernel . ĠSimilar Ġtrends Ġare Ġseen Ġin Ġthe Ġperformance Ġof Ġother Ġtwo Ġstate - of - the - art Ġapproaches ĠB IB REF 9 Ġ, ĠB IB REF 8 Ġ. ĠThus , Ġwe Ġdecide Ġto Ġperform Ġtraining Ġon ĠDat as et Ġ3 Ġand Ġtest Ġon Ġthe ĠDat as et Ġ1 . ĠAs Ġexpected Ġbetter Ġperformance Ġis Ġobtained Ġwith ĠF 1 - score Ġ76 . 78 %. ĠHowever , Ġthe Ġother Ġtwo Ġstate - of - the - art Ġapproaches Ġfail Ġto Ġperform Ġwell Ġin Ġthis Ġsetting . ĠWhile Ġthe Ġmethod Ġby ĠB IB REF 9 Ġob tains ĠF 1 - score Ġof Ġ47 . 32 %, Ġthe Ġapproach Ġby ĠB IB REF 8 Ġachieves Ġ53 . 02 % ĠF 1 - score Ġwhen Ġtrained Ġon ĠDat as et Ġ3 Ġand Ġtested Ġon ĠDat as et Ġ1 . ĠBelow , Ġwe Ġdiscuss Ġabout Ġthis Ġgeneral iz ability Ġissue Ġof Ġthe Ġmodels Ġdeveloped Ġor Ġreferred Ġin Ġthis Ġpaper . </s> As Ġdiscussed Ġin Ġthe Ġintroduction , Ġsarc asm Ġis Ġvery Ġmuch Ġtopic - dependent Ġand Ġhighly Ġcontextual . ĠFor Ġexample , Ġlet Ġus Ġconsider Ġthe Ġtweet ĠâĢ ľ I Ġam Ġso Ġglad Ġto Ġsee ĠTanzania Ġplayed Ġvery Ġwell , ĠI Ġcan Ġnow Ġsleep Ġwell Ġ: P ". ĠUnless Ġone Ġknows Ġthat ĠTanzania Ġactually Ġdid Ġnot Ġplay Ġwell Ġin Ġthat Ġgame , Ġit Ġis Ġnot Ġpossible Ġto Ġspot Ġthe Ġsarcastic Ġnature Ġof Ġthis Ġsentence . ĠThus , Ġan Ġn - gram Ġbased Ġsarc asm Ġdetector Ġtrained Ġat Ġtime ĠIN LINE FORM 0 Ġmay Ġperform Ġpoorly Ġto Ġdetect Ġsarc asm Ġin Ġthe Ġtweets Ġcrawled Ġat Ġtime ĠIN LINE FORM 1 Ġ( given Ġthat Ġthere Ġis Ġa Ġconsiderable Ġgap Ġbetween Ġthese Ġtime Ġstamps ) Ġbecause Ġof Ġthe Ġdiversity Ġof Ġthe Ġtopics Ġ( new Ġevents Ġoccur , Ġnew Ġtopics Ġare Ġdiscussed ) Ġof Ġthe Ġtweets . ĠSent iment Ġand Ġother Ġcontextual Ġclues Ġcan Ġhelp Ġto Ġspot Ġthe Ġsarcastic Ġnature Ġin Ġthis Ġkind Ġof Ġtweets . ĠA Ġhighly Ġpositive Ġstatement Ġwhich Ġends Ġwith Ġa Ġemot icon Ġexpressing Ġjoke Ġcan Ġbe Ġsarcastic . </s> State - of - the - art Ġmethods Ġlack Ġthese Ġcontextual Ġinformation Ġwhich , Ġin Ġour Ġcase , Ġwe Ġextract Ġusing Ġpre - trained Ġsentiment , Ġemotion Ġand Ġpersonality Ġmodels . ĠNot Ġonly Ġthese Ġpre - trained Ġmodels , Ġthe Ġbaseline Ġmethod Ġ( bas eline ĠCNN Ġarchitecture ) Ġperforms Ġbetter Ġthan Ġthe Ġstate - of - the - art Ġmodels Ġin Ġthis Ġgeneral iz ability Ġtest Ġsetting . ĠIn Ġour Ġgeneral iz ability Ġtest , Ġwhen Ġthe Ġpre - trained Ġfeatures Ġare Ġused Ġwith Ġbaseline Ġfeatures , Ġwe Ġget Ġ4 . 19 % ĠF 1 - score Ġimprovement Ġover Ġthe Ġbaseline Ġfeatures . ĠOn Ġthe Ġother Ġhand , Ġwhen Ġthey Ġare Ġnot Ġused Ġwith Ġthe Ġbaseline Ġfeatures , Ġtogether Ġthey Ġproduce Ġ64 . 25 % ĠF 1 - score . </s> Another Ġimportant Ġfact Ġis Ġthat Ġan Ġn - gram s Ġmodel Ġcannot Ġperform Ġwell Ġon Ġunseen Ġdata Ġunless Ġit Ġis Ġtrained Ġon Ġa Ġvery Ġlarge Ġcorpus . ĠIf Ġmost Ġof Ġthe Ġn - gram s Ġextracted Ġfrom Ġthe Ġunseen Ġdata Ġare Ġnot Ġin Ġthe Ġvocabulary Ġof Ġthe Ġalready Ġtrained Ġn - gram s Ġmodel , Ġin Ġfact , Ġthe Ġmodel Ġwill Ġproduce Ġa Ġvery Ġsparse Ġfeature Ġvector Ġrepresentation Ġof Ġthe Ġdataset . ĠInstead , Ġwe Ġuse Ġthe Ġword 2 vec Ġembed d ings Ġas Ġthe Ġsource Ġof Ġthe Ġfeatures , Ġas Ġword 2 vec Ġallows Ġfor Ġthe Ġcomputation Ġof Ġsimilarities Ġbetween Ġunseen Ġdata Ġand Ġtraining Ġdata . </s> Bas eline ĠFeatures Ġvs ĠPre - trained ĠFeatures </s> Our Ġexperimental Ġresults Ġshow Ġthat Ġthe Ġbaseline Ġfeatures Ġoutper form Ġthe Ġpre - trained Ġfeatures Ġfor Ġsarc asm Ġdetection . ĠHowever , Ġthe Ġcombination Ġof Ġpre - trained Ġfeatures Ġand Ġbaseline Ġfeatures Ġbeats Ġboth Ġof Ġthemselves Ġalone . ĠIt Ġis Ġcounter intuitive , Ġsince Ġexperimental Ġresults Ġprove Ġthat Ġboth Ġof Ġthose Ġfeatures Ġlearn Ġalmost Ġthe Ġsame Ġglobal Ġand Ġcontextual Ġfeatures . ĠIn Ġparticular , Ġbaseline Ġnetwork Ġdominates Ġover Ġpre - trained Ġnetwork Ġas Ġthe Ġformer Ġlearns Ġmost Ġof Ġthe Ġfeatures Ġlearned Ġby Ġthe Ġlatter . ĠNonetheless , Ġthe Ġcombination Ġof Ġbaseline Ġand Ġpre - trained Ġclass ifiers Ġimproves Ġthe Ġoverall Ġperformance Ġand Ġgeneral iz ability , Ġhence Ġproving Ġtheir Ġeffectiveness Ġin Ġsarc asm Ġdetection . ĠExperimental Ġresults Ġshow Ġthat Ġsentiment Ġand Ġemotion Ġfeatures Ġare Ġthe Ġmost Ġuseful Ġfeatures , Ġbesides Ġbaseline Ġfeatures Ġ( Figure ĠFIG REF 36 Ġ). ĠTherefore , Ġin Ġorder Ġto Ġreach Ġa Ġbetter Ġunderstanding Ġof Ġthe Ġrelation Ġbetween Ġpersonality Ġfeatures Ġamong Ġthemselves Ġand Ġwith Ġother Ġpre - trained Ġfeatures , Ġwe Ġcarried Ġout ĠSpear man Ġcorrelation Ġtesting . ĠResults , Ġdisplayed Ġin ĠTable ĠT AB REF 39 Ġ, Ġshow Ġthat Ġthose Ġfeatures Ġare Ġhighly Ġcorrelated Ġwith Ġeach Ġother . </s> Conclusion </s> In Ġthis Ġwork , Ġwe Ġdeveloped Ġpre - trained Ġsentiment , Ġemotion Ġand Ġpersonality Ġmodels Ġfor Ġidentifying Ġsarcastic Ġtext Ġusing ĠCNN , Ġwhich Ġare Ġfound Ġto Ġbe Ġvery Ġeffective Ġfor Ġsarc asm Ġdetection . ĠIn Ġthe Ġfuture , Ġwe Ġplan Ġto Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġproposed Ġmethod Ġon Ġa Ġlarge Ġcorpus Ġand Ġother Ġdomain - dependent Ġcorpor a . ĠFuture Ġwork Ġwill Ġalso Ġfocus Ġon Ġanalyzing Ġpast Ġtweets Ġand Ġactivities Ġof Ġusers Ġin Ġorder Ġto Ġbetter Ġunderstand Ġtheir Ġpersonality Ġand Ġprofile Ġand , Ġhence , Ġfurther Ġimprove Ġthe Ġdis amb ig uation Ġbetween Ġsarcastic Ġand Ġnon - s ar cast ic Ġtext .