Sentiment Analysis for Social Media

154 Pages·2020·6.61 MB·English

by  Carlos A. Iglesias (editor), Antonio Moreno (editor)

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Sentiment Analysis
for Social Media
Edited by
Carlos A. Iglesias and Antonio Moreno
Printed Edition of the Special Issue Published in Applied Sciences
www.mdpi.com/journal/applsci
Sentiment Analysis for Social Media

Sentiment Analysis for Social Media
SpecialIssueEditors
CarlosA.Iglesias
AntonioMoreno
MDPI•Basel•Beijing•Wuhan•Barcelona•Belgrade•Manchester•Tokyo•Cluj•Tianjin
SpecialIssueEditors
CarlosA.Iglesias AntonioMoreno
DepartamentodeIngenier´ıade Departamentd’Enginyeria
SistemasTelema´ticos, Informa`ticaiMatema`tiques,
ETSITelecomunicacio´n EscolaTe`cnicaSuperior
Spain d’Enginyeria,UniversitatRovira
iVirgili(URV)
Spain
EditorialOffice
MDPI
St.Alban-Anlage66
4052Basel,Switzerland
This is a reprint of articles from the Special Issue published online in the open access journal
AppliedSciences (ISSN 2076-3417) (available at: https://www.mdpi.com/journal/applsci/special
issues/SentimentSocialMedia).
Forcitationpurposes,citeeacharticleindependentlyasindicatedonthearticlepageonlineandas
indicatedbelow:
LastName,A.A.; LastName,B.B.; LastName,C.C.ArticleTitle. JournalNameYear,ArticleNumber,
PageRange.
ISBN978-3-03928-572-3(Pbk)
ISBN978-3-03928-573-0(PDF)
(cid:2)c 2020bytheauthors. ArticlesinthisbookareOpenAccessanddistributedundertheCreative
Commons Attribution (CC BY) license, which allows users to download, copy and build upon
publishedarticles,aslongastheauthorandpublisherareproperlycredited,whichensuresmaximum
disseminationandawiderimpactofourpublications.
ThebookasawholeisdistributedbyMDPIunderthetermsandconditionsoftheCreativeCommons
licenseCCBY-NC-ND.
Contents
AbouttheSpecialIssueEditors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
CarlosA.IglesiasandAntonioMoreno
SentimentAnalysisforSocialMedia
Reprintedfrom:Appl.Sci.2019,9,5037,doi:10.3390/app9235037. . . . . . . . . . . . . . . . . . . 1
HyojiHa,HyunwooHan,SeongminMun,SungyunBae,JihyeLeeandKyungwonLee
An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment
WordofMovieReviewData
Reprintedfrom:Appl.Sci.2019,9,2419,doi:10.3390/app9122419. . . . . . . . . . . . . . . . . . . 5
HannahKimandYoung-SeobJeong
SentimentClassificationUsingConvolutionalNeuralNetworks
Reprintedfrom:Appl.Sci.2019,9,2347,doi:10.3390/app9112347. . . . . . . . . . . . . . . . . . . 31
XingliangMao,ShuaiChang,JinjingShi,FangfangLiandRonghuaShi
Sentiment-AwareWordEmbeddingforEmotionClassification
Reprintedfrom:Appl.Sci.2019,9,1334,doi:10.3390/app9071334. . . . . . . . . . . . . . . . . . . 45
MohammedJabreelandAntonioMoreno
ADeepLearning-BasedApproachforMulti-LabelEmotionClassificationinTweets
Reprintedfrom:Appl.Sci.2019,9,1123,doi:10.3390/app9061123. . . . . . . . . . . . . . . . . . . 59
ElineM.vandenBroek-AltenburgandAdamJ.Atherly
UsingSocialMediatoIdentifyConsumers’SentimentstowardsAttributesofHealthInsurance
duringEnrollmentSeason
Reprintedfrom:Appl.Sci.2019,9,2035,doi:10.3390/app9102035. . . . . . . . . . . . . . . . . . . 75
SungheeParkandJiyoungWoo
GenderClassificationUsingSentimentAnalysisandDeepLearninginaHealthWebForum
Reprintedfrom:Appl.Sci.2019,9,1249,doi:10.3390/app9061249. . . . . . . . . . . . . . . . . . . 85
HuiLiu,YinghuiHuang,ZichaoWang,KaiLiu,XiangenHuandWeijunWang
PersonalityorValue:AComparativeStudyofPsychographicSegmentationBasedonanOnline
ReviewEnhancedRecommenderSystem
Reprintedfrom:Appl.Sci.2019,9,1992,doi:10.3390/app9101992. . . . . . . . . . . . . . . . . . . 97
GuadalupeObduliaGutie´rrez-Esparza,MaiteVallejo-AllendeandJose´Herna´ndez-Torruco
ClassificationofCyber-AggressionCasesApplyingMachineLearning
Reprintedfrom:Appl.Sci.2019,9,1828,doi:10.3390/app9091828. . . . . . . . . . . . . . . . . . . 125
v

About the Special Issue Editors
Carlos A. Iglesias (Professor). Prof. Carlos Iglesias Associate Professor at the Universidad
Polite´cnica de Madrid. He holds a Ph.D. in Telecommunication Engineering. He was previously
DeputyDirectoratGrupoGesforandInnovationDirectoratGerminusXXI.Hehasbeenactively
involved in research projects funded by private companies as well as national and European
programs. His research interests are focused on intelligent systems (knowledge engineering,
multi-agentsystems,machinelearning,andnaturallanguageprocessing).
Antonio Moreno (Professor). Dr. Moreno is a Full Professor of Artificial Intelligence at
University Rovira i Virgili (URV) in Tarragona, Spain. He was the founder and director of the
ITAKA(IntelligentTechnologiesforAdvancedKnowledgeAcquisition)researchgroupuntil2019.
Since2018, hehasbeentheDeputyDirectorofURVEngineeringSchool. Hehasbeentheauthor
ofmorethan60journalpapersandover125conferencepapers. Hehassupervised8Ph.D.theses
on different topics, including ontology learning, agents applied in health care, intelligent data
analysisappliedonhealthcaredata,recommendersystems,andmulti-criteriadecisionmaking. His
currentresearchinterestsarefocusedonsentimentanalysis,recommendersystems,andmulti-criteria
decisionsupportsystems.
vii

applied
sciences
Editorial
Sentiment Analysis for Social Media
CarlosA.Iglesias1,*,†andAntonioMoreno2,†
1 IntelligentSystemsGroup,ETSITelecomunicación,Avda.Complutense30,28040Madrid,Spain
2 IntelligentTechnologiesforAdvanceKnowledgeAcquisition(ITAKA)Group,
EscolaTècnicaSuperiord’Enginyeria,Departamentd’EnginyeriaInformàticaiMatemàtiques,
UniversitatRoviraiVirgili,43007Tarragona,Spain;[email protected]
* Correspondence:[email protected];Tel.:[phone]
† Theseauthorscontributedequallytothiswork.
(cid:2)(cid:3)(cid:4)(cid:2)(cid:5)(cid:6)(cid:7)(cid:8)(cid:9)
Received:31October2019;Accepted:19November2019;Published:22November2019 (cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)
Abstract: Sentimentanalysishasbecomeakeytechnologytogaininsightfromsocialnetworks.
Thefieldhasreachedalevelofmaturitythatpavesthewayforitsexploitationinmanydifferent
fieldssuchasmarketing,health,bankingorpolitics.Thelatesttechnologicaladvancements,suchas
deeplearningtechniques,havesolvedsomeofthetraditionalchallengesintheareacausedbythe
scarcityoflexicalresources.InthisSpecialIssue,differentapproachesthatadvancethisdiscipline
arepresented. Thecontributedarticlesbelongtotwobroadgroups: technologicalcontributions
andapplications.
Keywords:sentimentanalysis;emotionanalysis;socialmedia;affectcomputing
1.Introduction
Sentimentanalysistechnologiesenabletheautomaticanalysisoftheinformationdistributed
throughsocialmediatoidentifythepolarityofpostedopinions[1]. Thesetechnologieshavebeen
extendedinthelastyearstoanalyzeotheraspects,suchasthestanceofausertowardsatopic[2]
ortheusers’emotions[3],evencombiningtextanalyticswithotherinputs,includingmultimedia
analysis[4]orsocialnetworkanalysis[5].
ThisSpecialIssue“SentimentAnalysisforSocialMedia"aimstoreflectrecentdevelopmentsin
sentimentanalysisandtopresentnewadvancesinsentimentanalysisthatenablethedevelopment
offuturesentimentanalysisandsocialmediamonitoringmethods.Thefollowingsectionsdetailthe
selectedworksinthedevelopmentofnewtechniquesaswellasnewapplications.
2.NewPathsinSentimentAnalysisonSocialMedia
Traditionally,sentimentanalysishasfocusedontextanalysisusingNaturalLanguageProcessing
andfeature-basedMachineLearningtechniques.TheadvancesindisciplinessuchasBigDataand
DeepLearningtechnologieshaveimpactedandbenefitedtheevolutionofthefield.ThisSpecialIssue
includesfourworksthatproposenoveltechniques.
Inthefirstwork,titled“AnImprovedStudyofMultilevelSemanticNetworkVisualizationforAnalyzing
SentimentWordofMovieReviewData”[6],Haetal.proposeamethodforsentimentvisualizationin
massivesocialmedia. Forthispurpose,theydesignamulti-levelsentimentnetworkvisualization
mechanismbasedonemotionalwordsinthemoviereviewdomain.Theyproposethreevisualization
methods:aheatmapvisualizationofthesemanticwordsofeverynode,atwo-dimensionalscaling
mapofsemanticworddata,andaconstellationvisualizationusingasterismimagesforeachcluster
ofthenetwork.Theproposedvisualizationshavebeenusedasarecommendersystemthatsuggest
movieswithsimilaremotionstothepreviouslywatchedones. Thisnovelideaofrecommending
contentsbasedonsimilaremotionalpatternscanbeappliedtoothersocialnetworks.
Appl.Sci.2019,9,5037;doi:10.3390/app9235037 1 www.mdpi.com/journal/applsci