Sentiment Analysis in Social Networks

262 Pages·2017·10.41 MB·English

by  Federico Alberto Pozzi

Sentiment Analysis
in Social Networks
Sentiment Analysis
in Social Networks
Edited by
Federico Alberto Pozzi
Elisabetta Fersini
Enza Messina
Bing Liu
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Contributors
F.Bisio
UniversityofGenoa,Genoa,Italy
A.Bittar
HolmesSemanticSolutions,Gières,France
P.Buitelaar
NationalUniversityofIreland,Galway,Ireland
E.Cambria
NanyangTechnologicalUniversity,Singapore,Singapore
G.Carenini
UniversityofBritishColumbia,Vancouver,BC,Canada
P.Cipresso
AppliedTechnologyforNeuro-PsychologyLab,IRCCSIstitutoAuxologicoItaliano,Milan,Italy
L.Dini
HolmesSemanticSolutions,Gières,France
M.Dinsoreanu
TechnicalUniversityofCluj-Napoca,Cluj-Napoca,Romania
G.Fei
UniversityofIllinoisatChicago,Chicago,IL,UnitedStates
E.Fersini
UniversityofMilano-Bicocca,Milan,Italy
D.I.HernándezFarias
TechnicalUniversityofValencia,Valencia,Spain;UniversityofTurin,Turin,Italy
B.H.B.Honan
KRCResearch,NewYork,NY,UnitedStates
E.Hoque
UniversityofBritishColumbia,Vancouver,BC,Canada
I.Iennaco
EngineeringIngegneriaInformatica,Turin,Italy
C.A.Iglesias
TechnicalUniversityofMadrid,Madrid,Spain
C.Lemnaru
TechnicalUniversityofCluj-Napoca,Cluj-Napoca,Romania
H.Li
UniversityofIllinoisatChicago,Chicago,IL,UnitedStates
xi
xii Contributors
B.Liu
UniversityofIllinoisatChicago,Chicago,IL,UnitedStates
F.Mantovani
UniversityofMilano-Bicocca,Milan,Italy
E.Messina
UniversityofMilano-Bicocca,Milan,Italy
K.Moilanen
TheySayLtd.,London;UniversityofOxford,Oxford,UnitedKingdom
M.Montaner
StrategicAttentionManagement,Girona,Spain
G.Murray
UniversityoftheFraserValley,Abbotsford,BC,Canada
S.Negi
NationalUniversityofIreland,Galway,Ireland
M.Nissim
UniversityofGroningen,Groningen,TheNetherlands
L.Oneto
UniversityofGenoa,Genoa,Italy
F.Pallavicini
UniversityofMilano-Bicocca,Milan,Italy
P.Parau
TechnicalUniversityofCluj-Napoca,Cluj-Napoca,Romania
V.Patti
UniversityofTurin,Torino,Italy
L.Pernigotti
EngineeringIngegneriaInformatica,Bologna,Italy
R.Potolea
TechnicalUniversityofCluj-Napoca,Cluj-Napoca,Romania
F.A.Pozzi
SASInstituteSrl,Milan,Italy
S.Pulman
TheySayLtd.,London;UniversityofOxford,Oxford,UnitedKingdom
D.Richer
KRCResearch,NewYork,NY,UnitedStates
C.Robin
HolmesSemanticSolutions,Gières,France
Contributors xiii
P.Rosso
TechnicalUniversityofValencia,Valencia,Spain
J.F.Sánchez-Rada
TechnicalUniversityofMadrid,Madrid,Spain
S.Scamuzzo
EngineeringIngegneriaInformatica,Assago(MI),Italy
F.Segond
ViseoTechnologies,Grenoble,France
G.Vulcu
NationalUniversityofIreland,Galway,Ireland
Editors’ Biographies
Dr. Federico Alberto Pozzi received his PhD degree in computer science from the University of
Milano-Bicocca(Milan,Italy).HisPhDthesisfocusedonprobabilisticrelationalmodelsforsentiment
analysisinsocialnetworks.Hisresearchinterestsprimarilyfocusondatamining,textmining,machine
learning, natural language processing, and social network analysis, in particular applied to sentiment
analysisandcommunitydiscoveryinsocialnetworks.HecurrentlyworksatSASInstitute(Italy)asa
seniorsolutionsspecialistintheareaofintegratedmarketingmanagementandanalytics.
Dr.ElisabettaFersiniiscurrentlyapostdoctoralresearchfellowattheUniversityofMilano-Bicocca.
Her research activity is mainly focused on statistical relational learning, with particular interest in
supervisedandunsupervisedclassification.Theresearchactivityfindsapplicationinweb/textmining,
sentimentanalysis,socialnetworkanalysis,e-justice,andbioinformatics.Shehasactivelyparticipated
in several national and international research projects. She has been an evaluator for international
researchprojectsandamemberofseveralscientificcommittees.Shecofoundedanacademicspin-off
specializedinsentimentanalysisandcommunitydiscoveryinsocialnetworks.
Dr. Enza Messina is a professor in operations research in the Department of Informatics, Systems,
and Communication of the University of Milano-Bicocca, where she leads the research laboratory
MIND(“ModelsinDecisionMakingandDataAnalysis”).SheholdsaPhDdegreeincomputational
mathematics and operations research from the University of Milano. Her research activity is mainly
focused on decision models under uncertainty and more recently on statistical relational models for
data analysis and knowledge extraction. In particular, she has developed relational classification and
clusteringmodelsthatfindapplicationindomainssuchassystemsbiology,e-justice,textmining,and
socialnetworkanalysis.
Dr. Bing Liu is a professor of computer science at the University of Illinoisat Chicago. He received
hisPhDdegreeinartificialintelligencefromtheUniversityofEdinburgh.Hiscurrentresearchinterests
include sentiment analysis and opinion mining, data mining, machine learning, and natural language
processing. He has published extensively at top conferences and in journals, and is the author of
threebooks:SentimentAnalysisandOpinionMining(2012),WebDataMining:ExploringHyperlinks,
Contents,andUsageData(firstedition,2007;secondedition,2011),andSentimentAnalysis:Mining
Opinions, Sentiments and Emotions (2015). Two of his articles received 10-year test-of-time awards
from KDD, the premier conference of data mining and big data. His research has also been cited on
thefrontpageoftheNewYorkTimes.HecurrentlyservesasChairoftheAssociationforComputing
Machinery’s Special Interest Group on Knowledge Discovery, and is a fellow of the Association for
ComputingMachinery,theAssociationfortheAdvancementofArtificialIntelligence,andtheInstitute
ofElectricalandElectronicsEngineers.
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Preface
Thegreatdiffusionofsocialnetworksandtheirroleinmodernsocietyareamongthemoreinteresting
noveltiesinrecentyears,capturingtheinterestofresearchers,journalists,companies,andgovernments.
Thedenseinterconnectionthatoftenarisesamongactiveusersgeneratesadiscussionspacethatisable
to motivate and involve individuals, linking people with common objectives and facilitating diverse
forms of collective action. This gives rise to what is called “individualism on the net”: instead of
always counting on a single reference community, thanks to the social networks it becomes possible
tobestimulatedbymovingamongmorepeopleandresources,whichareoftenheterogeneous.Social
networks are therefore creating a digital revolution. The most interesting aspect of this change is not
solelyrelatedtothepossibilityofpromotingpoliticalparticipationandactivism.Thissocialrevolution
influencesthelivesofeveryindividual.Itisthefreedomtoexpressourselves,tohaveourownspacein
whichwecanbeourselves,ortobewhowewouldliketobe,withfewlimitsandbarriers.
The social network revolution enables us to talk about our emotions and opinions not only to
ourselves but especially to those around us, interacting with them, opening a window on the worlds
ofothers,andsnoopingintotheirlives.Paradoxically,allthishappenswhilewearelivinginasociety
where it is even more difficult to know the names of our neighbors, and where the right to privacy
becomes an imperative to which we submit. Because of social media, we effectively end up telling
thewhole(ormost)ofourlife:happinessatthebirthofachild,angeratatraindelay,pre-Christmas
shopping,orthechoicemadeinthesecrecyofthevotingbooth.Itisthennotsurprisingthatresearchers
startedtodiscussthemethodstocapturethisvastseaofinformation.Thedataonthenet,ifproperly
collectedandanalyzed,allowsusnotonlytounderstandandexplainmanycomplexsocialphenomena
but also to predict them. In this context, sentiment analysis tries to make evident what people think
byprovidingrepresentations,models,andalgorithmsabletomovefrom“simpleunstructuredtext”to
“complexinsight.”Inthisbookwewanttoprovideacomprehensiveunderstandingofthistopic,with
aparticularemphasisonitsroleinsocialnetworks.
SentimentAnalysisinSocialNetworksisfirstofalldirectedatresearcherswithcomputerscience,
mathematics,andstatisticsskillsbothinacademiaandinindustry.Researchersinacademiacanbenefit
from a consolidated knowledge background to create innovative solutions tuned in the challenging
environment of social networks. On the other hand, companies and organizations can take advantage
ofthemostrecentstateofthearttoinnovateprocesses,products,andservicesandthereforeincrease
theircompetitiveness.
The book is organized as follows: Chapter 1 is intended to make the reader aware of the
challengesrelatedtosentimentanalysisinsocialnetworksandpreparethereaderforafullandprecise
understandingoftheproblemsandsolutionspresentedinthenextchapters.
Chapter 2 introduces the psychological and sociological processes underlying social network
interactions. The chapter starts by highlighting the differences and specific features that characterize
online social network dynamics and finally points out how this understanding can be effectively
exploitedbysentimentanalysismethodologicalapproaches.
Chapter3describestheroleofsemanticsinsentimentanalysisbyanalyzingtwomainperspectives:
thewaysemanticsisencodedinsentimentresources,suchaslexica,corpora,andontologies,andthe
wayitisusedbyautomaticsystemsthatperformsentimentanalysisonsocialnetworkdata.
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xviii Preface
Chapter 4 highlights the main issues related to the interoperability of language resources for
sentiment analysis. After an introduction to the linked data perspective for semantic modeling of
sentiment and emotions, the chapter presents a linked data model based on two vocabularies: Marl
andOnyx.
Chapter 5 presents the most recent state of the art with regard to the affective characterization
of sentiment in social networks, then highlighting how affective reasoning can be employed for the
developmentofapplicationsinthecontextofbigsocialdataanalysis.
Chapter 6 discusses a machine learning approach to sentiment analysis in social networks, by
distinguishing between supervised, unsupervised, and semisupervised models and highlighting the
potentialleveragethatthenetworkstructurecanhave.
Chapter7introducesironyandsarcasmwhendealingwithinformaltextonthenetwork,underlining
thedifficultiesthatthesefiguresofspeechcancauseforunderstandingtherealsentimentofeveryday
communications.
Chapter 8 presents a comprehensive overview of a novel task related to suggestion mining from
opinionated text. Various aspects are discussed, including the analysis of suggestions appearing in
reviews,therelationbetweensentimentsandsuggestions,andthemostrecentmethodsforaddressing
theproblem.
Chapter 9 introduces the problem of opinion spam detection. After an overview of the problem,
differenttechniquestoleveragetherelationshipsbetweendifferententitiesinthenetworkarepresented
forthedetectionofbothopinionspamandopinionspammers.
Chapter 10 presents a comprehensive analysis of the state of the art related to opinion leader
detectionstrategiesandtheassociatedchallenges.Acriticaldiscussionoftheadvantages,limitations,
and suitability of the approaches is provided, illustrating the potential for generating added value in
differentscenarios.
Chapter 11 surveys the techniques for both extractive and abstractive summarization of opinion-
filled text, including a discussion of summarization evaluation. Then approaches to show opinion
summaries to the users in the form of visualizations are discussed, including several interactive
solutions.
Inthefollowingchapters,contributionsfromsmall-andmedium-sizedenterprisesrelatedtotheir
solutionstosentimentanalysisinsocialnetworksarepresented.Acomprehensivereviewofsoftware
tools and environments (both proprietary and open source) is presented and some case studies are
outlined.
Chapter12presetsSpagoBI,whichisanentirelyopensourcebusinessintelligencesuite.Itoffers
a wide range of analytical tools to perform reporting, multidimensional analysis, key performance
indicator management, visualization through charts, dashboards and cockpits, ad hoc querying and
reporting,locationintelligence,andnetworkanalysis.
Chapter 13 details SOMA, which is a smart social customer relationship management tool for
companies aimed at monitoring and dealing with consumers’ complaints and interactions in social
networks,withaparticularfocusonbothsentimentandemotions.
Chapter 14 describes KRC’s proprietary social and digital media analytics suite, which is able to
combinehumanintelligencewithadvancedpredictivemodels.
Chapter15presentsTheySay,whichisamultidimensionalopinionstreamingarchitecture,powered
byalarge-scalecustomnaturallanguageprocessingpipelineforsentiment,affect,andirrealisanalysis
usedtomonitor,quantify,andestimatetheimpactofcorporateannouncements toproviderichprice-
sensitivefeedbackandinsights.
Finally,Chapter16presentsafinaldiscussion,concludingwithsomethoughtsandopinionsabout
futuredirections.
Acknowledgments
Thecompletionofthisbookwouldnothavebeenpossiblewithoutthecontributionofmanyindividuals,
to whom we express our appreciation and gratitude. Without their help the book might never have
becomeareality.Firstofall,wethankalltheprestigiousauthorswhoacceptedouroffertobepartof
thisbook.Weareverygratefulfortheirpraiseworthyworkandfortheprofessionalismandseriousness
theydemonstratedduringthepreparationofthisbook.Inparticular,wethankGiuseppeCarenini,Erik
Cambria, Stephen Pulman, Paolo Rosso, Delia Irazú Hernández Faria, Huayi Li, Sapna Negi, Carlos
A. Iglesias, Viviana Patti, Malvina Nissim, Stefano Scamuzzo, Fabrizia Mantovani, Bradley Honan,
LucaDini,KaroMoilanen,PaulParau,PaulBuitelaar,J.FernandoSánchez-Rada,GabiVulcu,Letizia
Pernigotti, Isabella Iennaco, Pietro Cipresso, Federica Pallavicini, Derek Richer, Gabriel Murray,
EnamulHoque,GeliFei,MiquelMontaner,AndréBittar,CécileRobin,FrédériqueSegond,Camelia
Lemnaru,MihaelaDinsoreanu,RodicaPotolea,FedericaBisio,andLucaOneto.
We also express our gratitude to the companies which agreed to provide important contributions
and to write about their good practices. On the publication side, it was a pleasure working with the
helpfulstaffofMorganKaufmann,animprintofElsevier.Wethankoureditorialprojectmanager,Amy
Invernizzi,whohasbeenveryhelpfulforthesuccessofthisbook.Ithasbeenawonderfulexperience
workingwithher.Finally,wethankallresearchers,scientistsandindustriesoperatinginthisfieldfor
theirimportantcontributionsintheliteratureregardingsentimentanalysis.
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