Table Of ContentSentiment/Subjectivity Analysis Survey for
Languages other than English
6
1
0
2 MohammedKorayem*,Khalifeh Aljadda*,and DavidCrandall**
g *CareerBuilder, GA.
u **SchoolofInformaticsand Computing
A
IndianaUniversity
5 Bloomington,IN
2
[email protected]
] mkorayem,[email protected]
L
C August26,2016
.
s
c
[
Abstract
3
v
7 Subjective and sentiment analysis have gained considerable attention recently. Most
8 oftheresourcesandsystemsbuiltsofararedoneforEnglish. Theneedfordesigning
0 systemsforotherlanguagesisincreasing. Thispapersurveysdifferentwaysusedfor
0
building systems for subjective and sentiment analysis for languages other than En-
0
glish. Therearethreedifferenttypesofsystemsusedforbuildingthesesystems. The
.
1
first(andthebest)oneisthelanguagespecificsystems. Thesecondtypeofsystemsin-
0
volvesreusingortransferringsentimentresourcesfromEnglishtothetargetlanguage.
6
1 Thethirdtypeofmethodsisbasedonusinglanguageindependentmethods.Thepaper
: presentsaseparatesectiondevotedtoArabicsentimentanalysis.
v
i
X
1 Introduction
r
a
Nowadays,theWeb hasbecomea readandwrite platformwhereusersarenolonger
consumersofinformationbutproducersofitaswell. User-generatedcontentwritten
innaturallanguagewithunstructuredfreetextisbecominganintegralpartoftheweb
mainly because of the dramatic increase of social network Web sites, video sharing
Websites,newsportals,onlinereviewssites,andonlineforumsandblogs. Becauseof
thisproliferationofuser-generatedcontent,WebContentMiningisgainingconsider-
ableattentionduetoitsimportanceformanybusinesses, governmentalagencies,and
institutions.
Sentiment analysis (also referred to as opinion mining) is a computationalstudy
of attitudes, views, and emotions found in texts. The texts could be any document
(e.g., comments, feedback, reviews or blogs). Sentiment analysis can be viewed as
1
aclassificationprocessthataimsatdeterminingwhetheracertaindocument/textwas
writtentopassapositiveoranegativeopinionaboutacertaintopic,product,orperson.
Thisprocessregardseachdocumentasabasicinformationunit. Theprocesshasbeen
referredtoas“thedocumentlevelsentimentclassification”wherethedocumentisseen
asanopinionatedproduct.Theanalysisorclassificationofsentimentonthesentential
levelisreferredtoas“sentence-levelsentimentclassification”[31].
Sentiment analysis is gaining vast attention because of the potentiality of using
opinionsummaryofalargenumberofpopulationinindustryaswellasinotherfields.
For instance, having thisopinionsummaryavailablecan enhancebusinesses as busi-
ness owners would have access to consumer opinions. Individuals can benefit from
this informationas they would be able to compare products. Thus, sentiment analy-
sismakesitpossibletosummarizetheopinionofpeopletowardsproductsaswellas
politicians.
Performing this type of analysis (either on the sentential level or the document
level)hasbeendoneusingtwotypesofclassifiers,Rule-basedclassifier[13,15,17,24,
52],andMachinelearningclassifiers[1,27,28,31,36,37,46]. Currently,mostofthese
systemsarebuiltforEnglish[27,31].
Thecurrentpaperattemptstoexploresentiment/subjectiveanalysissystemscreated
generally for languages other than English. A special attention is given to Arabic.
The paper aims at providingthe reader with informationaboutthe methodsused for
buildingsentimentanalysissystems.
Aftersurveyingthedifferentwaysusedforbuildingsentimentanalysissystemsfor
languagesotherthanEnglish,thepaperconcludeswithasuggestionabouttheoptimum
method(s)tobefollowed. Thebestmethodistheemploymentoftoolsthathavetodo
withlanguage-specificfeatures.Themainproblemwiththismethodisthatitcostsalot
tobuildresourcesforeachlanguage. Thesecondmethodistransferringthesentiment
knowledge from English into the target language. The final way is to use language
independentmethods.
Thepaperisdividedintofourparts.Thefirstpartcoversthelanguageindependent
methods.Thesecondsectionsurveyssentimenttransfermethodscreatedtotransferthe
sentimentfrom English to other languages. The third section exploressystems done
specifically for languages other than English. The last part focuses on the methods
usedforArabic.
2 Language-Independent Feature Selection/Extraction
Methods
OnewayofperformingsentimentanalysesforlanguagesotherthanEnglishorbuilding
systemsworkableformultiplelanguagesistoextractandselectfeaturesthatdonotde-
pendontheselanguages.Differentapproacheshavebeenfollowedtoselectandextract
thesefeatures:(1)WeightedEntropyGeneticalgorithms,(2)FeatureSubsumption,(3)
LocalGrammarbasedmethods, (4)PositionalFeaturesand(5) Commonseedsword
methods.Here,eachfeatureselection/extractionapproachisdescribedseparately.
2
2.1 Entropy WeightedGenetic Weighted
GeneticAlgorithmisanoptimizationtechniquethatcanbeusedforfeatureselection.
Entropy Weighted Genetic Weighted (EWGA) combines Information Gain (IG) and
genetic algorithms (GA) to select the features. EWGA proposed in [1] was used to
selectfeaturesofArabicandEnglish. IGiscombinedwitheachstepinthegenetical-
gorithmsprocess.Itisusedtoselecttheinitialsetoffeaturesfortheinitialstage.Also,
itisappliedduringthecross-overandmutationstages. Abbasietal. [1]presentedsen-
timent analysis system for Web forumsin multiple languagebased on EWGA. They
used two types of features, stylistics featuresand lexical features. Semantic features
were avoided because they are language deepened and need lexicon resources while
the limitation of their data prevents the use of linking features. They evaluate their
systemonabenchmarktestbedofmoviereviewsconsistingof1000positiveand1000
negativemoviereviews [29,30,32,48].
Importantly,theirsystemwhichisbasedonfeatureselectionmethodoutperforms
systemsin[29,30,32,48]. Usingthissystem,theyachievedanaccuracyrateof91%
while other systems achieved accuracy rates between 87-90% on the movie reviews
dataset. Theywerealsoabletoachieve92%accuracyrateonMiddleEasternforums
and90%onUSforumsusingEWGAfeatureselectionmethod.
2.2 Feature Subsumption for Sentiment Classification in Multiple
Languages
Another method for extracting and selecting the features is proposed by Zhai et al.
[51]. The authors proposed the feature “subsumption” method to extract and select
substring-groupfeatures. This methodwas appliedto Chinese, English andSpanish.
ThesystemdesignedbyZhaietal.consistsoffourprocesses:(1)Substringfeatureex-
traction,(2)termweighting,(3)featureselection,and(4)classification. Forextracting
substring-groupfeatures,theybuiltasuffixtreewithincorporatingtransductivelearn-
ing through considering unlabeled test documents for building the suffix tree. They
appliedfourdifferentweightingschemes(binary,three,tfandtfidf-c)andThe”tfidf-
c”outperformsallotherapproaches.The”tfidf-c”isextendedformthestandrad”tfidf”
andisdefinedasfollows 1
tf(t ,d )×log(N/df(t ))
k j k
tfidf −c= (1)
(tf(t ,d )×log(N/df(t )))2
k j k
stX∈dj
wheret representsthetermcorrespondingto thesinglefeatureandtf(t ,d )is the
k k j
termfrequencyforthetermkindocumentd,df(t )isthenumberofdocumentscon-
k
tainingthetermandN isthetotalnumberofdocuments. Termpresenceusuallyout-
performstermfrequency[33,51].
Zhai et al. [51] applieddocumentfrequencymethod as a feature selection tech-
nique by keeping the top N features with highest documentfrequencyscores. They
testedtheproposedsystemonthreedatasets: 1)anEnglishdatasetofmoviereviews,
2) a Chinese data set of hotel reviews, and 3) a Spanish data set of reviews on cars,
3
hotels,andotherproducts.Theaccuracyratesachievedwere94.0%,84.3%and78.7%
forChinese, English,andSpanishrespectively. Thissystemisa successif compared
tosystemsin[25,33]whichareusedfortheEnglishandChinesedatasets. However,
itwasoutperformedbyAbbasiandetal. [1]ontheEnglishdatasetdescribedinthe
previoussection.
2.3 Local GrammarMethods
LocalGrammarisanothermethodthatcanbeusedtoextractsentimentfeatures. Itis
usedtoextractsentimentphrasesinthefinancialdomain [8,9].
Ahmedetal.[9]proposedthisapproachforfinancialnewsdomain.Theyidentified
the interesting key words by comparing the distribution of words in financial news
corpuswiththedistributionofthesamewordsingenerallanguagecorpus. Usingthe
context around these words they built a local grammar to extract sentiment bearing
phrases. TheyappliedtheirapproachtoArabic,English,andChinese. Theyevaluated
the system manually and achievedaccuracy rates between 60-75%for extracting the
sentimentbearingphrases. Importantly,theproposedsystemcouldbeusedtoextract
thesentimentphrasesinthefinancialdomainforanylanguage.
Agic´etal.[8]usedlocalgrammartoextractsentimentphrasesoffinancialarticles.
They demonstrated that there is a relation between the number of polarized phrases
andtheoverallsentimentofthearticle. Theybuilta“Goldensentimentanalysisdata
set”offinancialdomainforCroatian. Theymanuallyannotatedthearticleswithposi-
tive/negativeannotation.Someofthearticleswereannotatedatthephraselevel.
Importantly,whileBollenetal.[12]showedthatthereisacorrelationbetweencol-
lectivemoodstatesextractedfromlarge-scaleTwitterfeedsononehandandthevalue
oftheDowJonesIndustrialAverage(DJIA)overtimeontheother,Agic´etal. demon-
stratethatthereisastatisticallysignificantcorrelationbetweenthetotalmarkettrend
ontheZagrebStockExchangeandthenumberofpositivelyandnegativelyannotated
articles within the same periods. The corpus used for this analysis is collected from
two differentresources: online newspapersspecialized on finance and a large forum
discussing the Zagreb Stock Exchange (CROBEX). For CROBEX two long periods
oftimearechosen,oneforpositivearticlesbetween2007-01-02and2007-05-21and
theotherfornegativeonespublishedbetween2008-01-07and2008-04-16.Ofcourse,
thefinancialnewsdocumentsareselectedrandomlyfromthecorpusforthesametwo
periodsandannotatedmanually.
2.4 PositionalFeatureMethods
Positional information of the words and sentences has been used to build sentiment
systems. RaychevandNakov[34]proposedthelanguageindependentmethodwhich
isbasedonsubjectivityandpositionalinformation.
Specifically, they weighted unigram and bigram terms based on their position in
documents.Theyincorporatethesubjectivityinformationbyremovingnon-subjective
sentencesandthentheymovedthesubjectivesentencestotheendofthedocumentsby
computingthelikelihoodofsentencesubjectivity. ThiswasdonebytrainingaNaive
4
BayesclassifieronsubjectivedatasetandsortingthesentencesbasedontheirLikeli-
hoodsubjectivityscore.Theyevaluatetheirmethodonthestandardmoviereviewsdata
setusedin[48][30] [29] [32]. Theyachieved89.85%accuracyrateusingunigrams,
bigrams,subjectivityfilter,andsubjectivitysorting.
2.5 Commonseedwords methods
Usingveryfew commonwordslike “very,” “bad, ” and“ good”in English, a senti-
mentanalysissystem is built byLin et al. [26]. The authorsproposeda multilingual
sentiment system using few seed words which could be applied to any language be-
cause it is language independent and does not depend on features of any language.
First,theyextractedopinionwordsbasedontheassumptionthatthereisanadverbof
degree on each language( e.g “very” in English). They extracted words by heuristic
informationbasedonpatternslike“wordbehindvery”andremovingstopwordsbased
onfrequency. Thenextstepafterextractingopinionwordsistoclusteropinionwords
intopositiveandnegativeclusters.
To cluster the words, they proposed a simple and effective method consisting of
three steps: (1)Labeling all samples and wordsbased on two seed words“goodand
bad”, (2) Computingexclusivepolarityfor each opinionword using KL-divergentto
solvedisambiguationforwordsappearingin positiveandnegativeexamples, and(3)
Computingthenewlabelsforsamplesbasedonthecomputedpolarityofwords.
Aftercreatinglexiconsofpositiveandnegativewords,theyintroducedSemi-supervised
learningto build sentiment classifier. They evaluated the system using hotel reviews
data sets for many languages (French, German, Spanish, and Dutch). Their system
achieved accuracy rates (80.37%, 79.13%, 80.05%, and 81.33%) corresponding to
(French,German,Spanish,andDutch).
They compared their system to two baseline systems “Sentiment lexicon based
methods”and“Machinetranslationbasedmethods”. Whilethetranslationbasedsys-
tem outperformsthe lexiconbasedsystem, the proposedsystem outperformsthe two
baselines.
3 Sentiment Translation Methods
TransferringSentimentTranslationtechniquesofwell-studiedlanguagestonewones
isanotherwayforbuildingsentiment/subjectivitysystems. Simply,thesemethodsare
based on using machine translation techniquesto translate the resources(corpora)to
the new languages. Here, various sentiment/subjectivity methods based on machine
translationwillbesurveyed.Thetechniquesusedtosolvetheproblemsresultingfrom
non-accuratemachine translationprocesseswill be tackled. Other methodsbased on
graphmethodsandusedtotranslatesentimentwillalsobepresented.
3.1 Machine Translation
Machine translation (henceforth MT) has been used as a simple tool to create senti-
mentsystemsformultiplelanguages. Inthesesystems,MThasbeenused[15,28,45]
5
totranslatecorporaofdifferentlanguagesintoEnglish.Followingthetranslation,sub-
jectivity/sentimentclassifiers are builtin English. The simplicity of using MT stems
fromtheavailabilityofitstechniquesandtheavailabilityofEnglishResources. Also,
MTisusedtogenerateresourcesandcorporaforlanguageotherthanEnglish. Using
it, sentiment lexicons and corpora have been generated for Bengali, Czech, French,
German,Hungarian,Italian,Romanian,andSpanish[11,13,14,40,41].
3.1.1 MachineTranslationSSAsystems:
DuetothesimplicityandavailabilityofMT,Kerstin[15]proposedapipelinesystem
based on SenitwordNet [19] and MT techniques for multi-languages. The proposed
systemisapipelinesystemconsistingofthefollowingsteps:
• Languageclassification wherethe LingPipelanguageidentifieris used forlan-
guageClassification;
• TranslationofthedocumentfromtheidentifiedlanguagetoEnglish;
• textpreparationbystemming;and
• Classificationofthesentiment.
Simplicityandvariabilityareattributesofthedifferentwaysusedinbuildingtheclas-
sifiers. Forinstance,threedifferentwayswereusedinbuildingtheclassifiersin[15].
Thesewaysaremachinelearningclassifiers,lingpipeandrulebasedclassifiers.
Comparison of the three methods of classifier building shows that, the classifier
basedonmachinelearningprovidesthemostaccuraterates(thescoresofSentiwordNet
were 62% on MPQA corpus[49] and 66% for German movie reviews). In [15], the
proposedsystemissimpleandcouldbeappliedtoanylanguage.
Similarly,MTtechniquesarealsousedtobuildsentimentsystemsforChinese[45].
Wanetal.[45]usedtheautomaticmachinetranslationtechniquetotranslateChinese
documents into English. The English lexicon is used afterwards. Many methods to
assembletheresultsfrombothlanguagesweresuggested.Thesemethodsincludeaver-
age,weightedaverage,min,maxandmajorityvoting.Thesemanticorientationmethod
hasalsobeenusedtocomputethescoreofthedocumentsaswellasthewindowsize
inordertoenablehandlingnegation.
The obtainedEnglish results showed that using the translated reviews give better
results if comparedto the originalChinese ones. This situation stands in contrast to
whatmighthave beenexpected: The originallanguageusing originallexiconshould
havegivenbetterresultsifcomparedtothetranslatedone.Also,theensemblemethods
improvetheobtainedresults.
Another usage of MT is incorporatingfeatures from many languages to improve
theclassifiersaccuracy. Baneaetal.[10]integratedfeaturesfrommultiplelanguages
whenbuildingahighprecisionclassifierusingmajorityvote. Abasicsinglelanguage
trainedclassifierwasusedasabasisforthishighprecisionclassifier. Thesystemwas
evaluatedonMPQAcorpus.Theintegratedfeaturetechniquewasusedforsixdifferent
languages(Arabic,French,English,German,Romanian,andSpanish). Twotypesof
feature-sets (monolingual and multilingual) were used. The feature vector for each
6
sentenceofthemonolingualfeaturesetconsistsofunigramsforthislanguagewhilethe
featurevectorofthemultilingualfeaturesetconsistsofcombinationsofmonolingual
unigrams.
Importantly,resultsshowthatusingEnglishannotateddatasetscanbuildsuccess-
fulclassifiersforotherlanguagesbyleveragingtheannotateddataset.Thecreatedclas-
sifiershavemacro-accuracybetween71.30%to73.89%forArabicandEnglish.Here,
theEnglishclassifieroutperformedthoseforotherlanguages.Non-Englishbasedclas-
sifierresultsshowthatusingthemultilingualdatasetcanimprovetheaccuracyofthe
classifierforthesourcelanguageaswellasclassifiersforthetargetlanguages.Specif-
ically,thebestresultsareobtainedwhenaclassifiertrainedoverthecombinationofall
sixlanguageswasused[10].
Thissuggeststhatusingmultilanguagedatasetscanenrichthefeaturesandreduce
ambiguity.Inaddition,theEnglishclassifierachievedthebestaccuracyrateamongall
monolingualclassifiers. Also,wheninvestigatingthecombinationofanytwo-language
from the six languages, the German and Spanish classifier achieved the best results.
PerformanceincreasedwhenRomanianandArabicwereadded. AddingEnglishand
Frenchdidnotimprovetheresults. Indeed,theseresultssuggestthatSpanishandGer-
manexpandedthedimensionalitycoveredinEnglish,ArabicandRomanianbyadding
high quality features for the classification task. They also showed that the majority
votingclassifiercouldbeusedasahighprecisionclassifierwithacceptablerecalllevel
bycombiningallmonolingualclassifiers.
3.1.2 MachineTranslationasaResourceGenerator
In addition to using MT as a technique in building sentiment/subjectivitysystems as
previously explained, it was used to create resources and dictionaries for the analy-
ses of sentiment in multiple languages. Mihalcea et al. employ two different ways
to generate resources for subjectivity in languagesby leveragingtools and resources
of English. The first method is translating an existing English lexicon to the target
languageusingbi-lingualdictionary. The secondmethodis a corpusbased approach
wheretheannotatedcorpusinthetargetlanguageisbuiltusingaprojectionfromthe
sourcelanguage[28].
In the first method, authors translate the target language lexicon using two bi-
lingual dictionaries [49]. Some problems emerged with this approach. First, some
wordslosttheirsubjectivityinthisprocess. Forexample,whentranslatingintoRoma-
nian,thewordmemorieslostitssubjectivityasitwastranslatedintothepowerofre-
tainingtheinformation.Second,therewerecasesoflackonthesenseoftheindividual
entriesinthelexiconandthebilingualdictionary.Third,somemulti-wordexpressions
werenottranslatedaccurately.Consequently,thisledtolosingthesubjectivityofsome
ofthesemultiwordexpressionsaftertranslation.
Trials to solve the first problemhave been introduced. In [14], researchersover-
came this obstacle by clustering the words that have the same Root. Then, the root
itself is checked against the English lexicon. If the root exists then the word is kept
in the list which will be translated. To overcome the second problem, heuristic ap-
proachesareused.Examplesoftheseheuristicapproachesareusingthemostfrequent
techniquein[28]andFirsttypeisFirstWord(FW)[24].Inthethirdproblem,asimple
7
wayforsolvingthemulti-wordexpressionissueisusingword-by-wordapproach[28]
andusingtheWebtovalidatethetranslationbycheckingitsoccurrenceintheWeb.
Evaluationofthemethodoftranslatingthelexiconusingbilingualdictionariesre-
flectsthat, thetranslatedlexiconislessreliablethantheEnglishone. Therulebased
classifierisusedtoevaluatethelexicon.Thisclassifierusesasimpleheuristic.Itlabels
thesentenceassubjectiveifitcontainstwoormorestrongsubjectiveexpressionsand
asobjectiveifitcontainsatmosttwoweaksubjectiveexpressions(nostrongsubjec-
tiveexpressionsatall). Otherthanthat,thesentenceislabeledasunknown. Thistype
ofclassifiersgenerallyhashighprecisionandlowrecallsoitcouldbeusedtocollect
sentencesfromunlabeledcorpus.
Importantly,therule-basedclassifierperformsbadlyintheobjectivetask. Onerea-
sonisthat,weaksubjectivitycluesloseitssubjectivityduringthetranslationprocess.
In[28], researchersworkedona manualannotationstudywhichshowedthata small
fractionofthetranslatedwordskeepitssubjectivityaftertranslation.
The second method is the corpus-based approach where the annotated corpus in
thetargetlanguageisbuiltusingprojectionfromthesourcelanguage. ThenMachine
learningclassifiersaretrainedonthelabeleddata. Theexperimentalresultsobtained
in applying this method show that generally machine learning classifiers outperform
therule-basedclassifier.
Toovercomechallengesmetincaseswherenobilingualdictionaryorparallelcor-
pora are available, Banea et al. [11] extend the work in [28] by employing multiple
waystoperformautomatictranslationfromEnglish.Thisisbasicallydonetogenerate
resourcesin the new language using English resources. They designed three experi-
mentstoevaluatewhetherautomatictranslationisa goodtoolforgeneratingnewre-
sources. Thefirstandsecondtypesofexperimentsaredonebytranslatingthetraining
sourceinto the targetlanguage. In the first experiment,the trainingdata is manually
annotated.Inthesecondone,opinionfinderclassifiersareusedtoannotatethecorpus
whentheannotationdoneisinthesentencelevel. Theobtainedresultsshowthatthe
automaticannotatedcorpusisworkingbetterthanthemanuallyannotatedcorpus.This
suggeststhatthecluesusedbyresearcherstoannotatethedatamightbelostduringthe
translationprocesswhilethecluesusedbyclassifiersarekeptduringthisprocess. In
the third experiment, the target language is translated into the source language then
theopinionfindertoolisusedtolabelthesentences. Followingthat,thesentencesare
projected back to the target language. Finally, the classifier is trained. The authors
evaluate the MT methods used for Romanian and Spanish. Results show that man-
ually or automated labeled data are sufficient to build tools for subjectivity analysis
in the new language. Furthermore, the results show comparable results to manually
translatedcorpora.
MThasalsobeenusedtogenerateresources,in [40,41]parallelcorporaforseven
languagesofsentimenttowardsentitiesarebuilt. Specifically,theGoldstandardsen-
timent data is built in English then projected into other languages (Czech, French,
German,Hungarian,Italian,andSpanish). Here,ageneralandsimplesentimentcom-
putingmethodhasbeenusedbycountingthenumberofsubjectivitywordswithinthe
window for a given entity [41]. The resourcesused were sentiment dictionaryavail-
ableinto15languages[40]. Negationishandledbyadding4toeachsentimentscore
ofnegatedwords(thesentimentscoreofeachwordisbetween-5and5).
8
Importantly, this system is language-independentbecause it depends only on the
lexicons.Thesystememployingthegoldenstandarddataachievedaccuracyratesfrom
66%(Italian)to74%for(EnglishandCzech).
As in [11], MT is used to translate English lexicon (a productof merging Senti-
Word English lexicon [19] and Subjectivity Word List [49]) into Bengali [14]. Das
andBandyopadhyay[14]usedmachinelearningclassifierswithmanyfeaturessuchas
partofspeechtaggingandchunkingtodivideeachdocumentintobeginning,interme-
diate,andend. Eachsentenceisthengivenafeatureindicatingwhetheritbelongsto
the beginning,intermediateorend. Theyalso used lexiconscoresasfeaturesto give
subjectivityscores of the word, stemming, frequency,positionof subjectivityclue in
thedocument,thetitleofthedocument,thefirstparagraphandthelasttwosentences.
Theoverallaccuracyrateofthesystemisfoundtobe76.08%precisionrate(PR)and
83.33%recallrate(RR) forMPQAdata set[49], and79,90(PR) and86,55(RR) for
IMDPcorpus,and72.16%(PR)and76.00%(RR)forBengaliNewscorpusand74.6%
(PR)and80.4%(RR)forBlogcorpus.
To recap, in thissectiondifferentmethodsforusingMT tobuild subjectivityand
sentimentsystemswerereviewed.Themainissuesthatemergedintheexperimentation
of MT havealso beenhighlighted. In the nextsection, the methodsdoneto improve
machinetranslationSSAsystemswillbereviewed.
3.2 Improving Machine Translation-basedSystems
TwomethodshavebeenbuilttoimprovemachinetranslationSSAsystems.Mainlythis
section describes the co-training [46] and the structural corresponding learning [47]
methods.
3.2.1 Co-trainingMethod
In [46], Wan introduces the co-training algorithm to overcome the problem of low
performanceofMT methodsused in [25]. Theproposedco-trainingframeworkuses
unlabeleddatainthetrainingprocess.Specifically,theco-trainingmethodmanipulates
twoviewsforlearning,thesourcelanguageviewaswellasthetargetlanguageview.
Here,twoseparateclassifiersaretrainedonlabeleddata,oneforthesourcelanguage
and the otherfor the targetone. Using of unlabeleddata comesafter havingthe two
classifiers. This is done by adding the most confident samples to the labeled set on
eachviewifthetwoclassifiersagree. Then,theclassifiersareretrained. Theoutcome
wouldbetwodifferentclassifiers. Thepredictionofthesentimentwillbebasedupon
thescoreofthetwoclassifiers(e.gaverageofthescoresofthetwoclassifiers).
Theobtainedexperimentalresultsshowthattheco-trainingalgorithmoutperforms
theinductiveandtransductiveclassifiers[46].Thisframeworkwastestedonsentiment
classificationforChinesereviews. Thefeaturesusedareunigramsandbigrams. The
term-frequencyis used to weight the features which works better than tf-idf in their
empiricalexperiments.
9
3.2.2 TheStructuralCorrespondingLearningMethod
In[47],researcherstrytoovercomethenoisecomingfromMTmethodsusedin[28]
byusingstructuralcorrespondinglearning(SCL)to findsharedimportantfeaturesin
the two languages. SCL is used for domain adaptations. Here, the authors suggest
thatthe sentimentclassification ofthe cross-lingualcouldbe consideredas a domain
adaptionproblem. TouseSCL,thefirststepistofindthesetofpivotfeatures. These
features/words have the same manner on the source and target language (e.g “very
good”and“perfect”).
SCL worksasfollows: First, itstartsbygeneratingtheweightedmatrixbasedon
theco-occurrencebetweenpivotfeaturesandordinaryfeatures. Second,singularvec-
tordecompressionisusedtoselectthetopeigenvectorfeaturestocreatethemapping
matrixfromoriginaldomainto lowerdimensiondomain. Third, the mappingmatrix
will be used with the new features in the new language/domain to train the classi-
fier. Theauthorskeptonlythepivotfeaturesonthetranslationprocessandthenused
weighted matrix from source language in addition to using the new translated pivot
featurestotraintheclassifier. Fortheselectionofthepivot,somewordsareselected
accordingtotheiroccurrence. Followingthatthesewords/featuresarerankedaccord-
ingtotheirconditionalprobabilitiesthatarecomputedonthelabeleddata.
Importantly,anevaluationoftheSCLisdoneforthesamedatasetusedon[46]The
resultsshowthatSCLoutperformstheco-training[46]intermsofF-measure(reported
tobe85%inthiscase).
3.3 GraphMethods forTranslating Sentiment
InadditiontousingMTfortranslatingandtransferringsentimentfromonelanguageto
another,Graphmethodshavebeenused. Scheibleetal.[38,39]usesthegraph-based
approachtotransfersentimentfromEnglishtoGerman. Theybuiltgraphscontaining
twotypesofrelations(coordinationsandadjective-nounmodifications). Theyspecif-
ically chose these types of relations as they contain clues for sentiment. The graph
contains adjectives and nouns as nodes and relations is represented by edges. They
built two graphs one for English and the other for German. To compute sentiment
of the target language, SimRank algorithm is used. SimRank computes the similar-
ity between nodesin the two graphs. SimRank algorithm is an iterative processthat
measuresthesimilaritybetweenallnodesinthegraph.SimRankassumesthatthetwo
nodesaresimilariftheirneighborsaresimilar. Similaritybetweentwonodesaandb
aredescribedbyequation 2
v
sim(a,b)= sim(i,j) (2)
|N(a)||N(b)|
i∈N(aX),,j∈N(b)
WhereN(a)istheneighborhoodgroupofa andv isa weightedvectortodetermine
theeffectofdistanceofneighborsofaandinitiallythesim(a,a)=1.
In this method, the bi-lingual lexicon is used to get the seeds between the two
graphs. TheexperimentsaredoneonEnglishandGermanversionsofWikipedia. The
resultsshow thatthis methodworksbetter thanthe Semantic Orientationwith Point-
10
