Socio-Affective Computing

196 Pages·2015·3.99 MB·English

Socio-Affective Computing
Volume 1
SeriesEditor
AmirHussain,UniversityofStirling,Stirling,UK
CoEditor
ErikCambria,NanyangTechnologicalUniversity,Singapore
This exciting Book Series aims to publish state-of-the-art research on socially
intelligent,affectiveandmultimodalhuman-machineinteractionandsystems.Itwill
emphasizetheroleofaffectinsocialinteractionsandthehumanisticsideofaffective
computing by promoting publications at the cross-roads between engineering and
human sciences (including biological, social and cultural aspects of human life).
Three broad domains of social and affective computing will be covered by the
book series: (1) social computing, (2) affective computing, and (3) interplay of
the first two domains(forexample,augmentingsocialinteractionthroughaffective
computing).Examplesofthefirstdomainwillincludebutnotlimitedto:alltypesof
social interactionsthatcontribute to the meaning,interestand richnessof our daily
life, for example, information produced by a group of people used to provide or
enhance the functioningof a system. Examplesof the second domain will include,
but not limited to: computational and psychological models of emotions, bodily
manifestations of affect (facial expressions, posture, behavior, physiology), and
affective interfaces and applications (dialogue systems, games, learning etc.). This
series will publish works of the highest quality that advance the understanding
and practical application of social and affective computing techniques. Research
monographs, introductory and advanced level textbooks, volume editions and
proceedingswillbeconsidered.
Moreinformationaboutthisseriesat http://www.springer.com/series/13199
Erik Cambria • Amir Hussain
Sentic Computing
A Common-Sense-Based Framework
for Concept-Level Sentiment Analysis
123
ErikCambria AmirHussain
SchoolofComputerEngineering ComputingScienceandMathematics
NanyangTechnologicalUniversity UniversityofStirling
Singapore,Singapore Stirling,UK
Socio-AffectiveComputing
ISBN978-3-319-23653-7 ISBN978-3-319-23654-4 (eBook)
DOI10.1007/978-3-319-23654-4
LibraryofCongressControlNumber:2015950064
SpringerChamHeidelbergNewYorkDordrechtLondon
©SpringerInternationalPublishingSwitzerland2015
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In memory of Dustin,
A man with a great mind and a big heart.

Foreword
Itwasa particularjoytomehavingbeenaskedtowritea fewwordsforthissecond
book on sentic computing - the first book published in 2012, gave me immense
inspirationwhichhasgrippedmeeversince.Thisalsomakesitarelativelyeasybet
thatitwillcontinueitswaytoastandardreferencethatwillhelpchangethewaywe
approachsentiment,emotion,andaffectinnaturallanguageprocessingandbeyond.
Whileapproachestointegrateemotionalaspectsinnaturallanguageunderstand-
ingdatebacktotheearly1980ssuchasinDyer’sworkonIn-DepthUnderstanding,
at the very turn of the last millennium,there was still very limited literature in this
direction. It was about 3 years after Picard’s 1997 field-defining book on Affective
Computing and one more after the first paper on Recognizing Emotion in Speech
by Dellaert, Polzin, and Waibel and a similar one by Cowie and Douglas-Cowie
that followed ground-laying work, including by Scherer and colleagues on vocal
expression of emotion and earlier work on synthesizing emotion in speech when a
globalindustrialplayerplacedanorderforastudywhetherwecanenablecomputers
to recognize users’ emotional factors in order to make human-computerdialogues
morenatural.
After first attempts to grasp emotion from facial expression, our team realized
that computer vision was not truly ready back then for “in the wild” processing.
Thus, the thought came to mind to train our one-pass top-down natural language
understandingengineto recognizeemotionfromspeechinstead. In doingso, I was
left with two options: train the statistical language model or the acoustic model to
recognize basic emotions rather than understand spoken content. I decided to do
both and, alas, it worked– at least to some degree.However,when I presented this
newability,theusualaudienceresponsewas,mainlyalongthelinesof“Interesting,
butwhatistheapplication?”Sincethen,amajorchangeofmindhastakenplace:it
isbyandlargeagreedthattakingintoaccountemotionsiskeyfornaturallanguage
processingandunderstanding,especiallyfortaskssuchassentimentanalysis.
As a consequence,these days, severalhundredpapersdealingwith the topic ap-
pearannually,andonefindsseveralthousandcitationseachyearin thisfield which
is still gaining momentum and expected to be nothing less than a game-changing
factor in addressing future computing challenges, such as when mining opinion,
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viii Foreword
retrieving information, or interacting with technical systems. Hardly surprisingly,
the commercial interest is ever rising, and first products-have already found their
wayintobroadpublicawareness.
The lion’s share of today’s work aimed at dealing with analyzing emotion
and sentiment in spoken and written language, is based on statistical word co-
occurrences. The principle is described in Joachim’s 1996 work on text catego-
rization representing a document as a “bag-of-words” in a vector space. Different
normalizations are named, and sequences of n words or characters (‘n-grams’)
have since been applied successfully in similar fashion. With the advent of “big
data,” recent approaches, such as by Google, translate the single words into (their
individual) vectors by (some form of) soft clustering. This reflects each word’s
relation to the other words in the vocabulary as added information. However, such
approaches have reached a certain glass ceiling over the years as they are very
limited in taking inspiration from how the human brain processes both emotions
(byexploitinganemotionmodel)andmeaning(byworkingatthesemantic/concept
levelratherthanatthesyntactic/wordlevel)toperformnaturallanguageprocessing
taskssuchasinformationextractionandsentimentanalysis.
This is what Sentic Computing is all about. Targeting the higher hanging fruits
bynotmissingtheimportanceofaimingtoemulatethebrain’sdescribedprocessing
ofemotionsandmeaning,itprovidesaknowledge-basedapproachtoconcept-level
sentimentanalysisthatiswellrootedinamulti-disciplinaryview.Theconsideration
of a text as a bag-of-wordsis accordingly substituted by representing it as a “bag-
of-concepts.” This embeds linguistics in an elegant form beyond mere statistics,
enrichingtherepresentationoftextbythedependencyrelationbetweenclauses.The
bookguidesitsreadersfromthestudent-levelonwardsinaningeniousfashion,from
an introduction and background knowledge (not only on sentiment analysis and
opinionmining but also commonsense) to the core piece – SenticNet (introducing
the acquisition and representation of knowledge as well as reasoning) to concept-
level sentiment analysis. It then exemplifiesthese ideas by three excellently picked
applications in the domains of the social web, human-computerinteraction, and e-
health systems before concluding remarks. Thus, besides providing the essential
comprehensionof the basicsof the field in a smoothandveryenjoyableway, itnot
only manages to take the reader to the next level but introduces genuine novelty
of utmost valuable inspiration to any expert in the field. In fact, it makes a major
contribution to the next generation of emotionally intelligent computer systems.
Do not be surprised catching yourself reasoning about sentiment and opinions in
a whole new way even in your “non-tech” life. It remains to say that I am truly
lookingforwardtothevolumestofollowthisonethatkicksofftheseriesonSocio-
Affective Computing edited by the authors – and sets the bar utmost high for all
aspiringreaders.
ImperialCollege,London,UK BjörnW. Schuller
July2015 President,Associationforthe Advancement
ofAffectiveComputing(AAAC)
Editor-in-Chief:IEEETransactions
onAffectiveComputing
Preface
The opportunity to capture the opinions of the general public has raised growing
interest both within the scientific community, leading to many exciting open
challenges, and in the business world due to the remarkable range of benefits
envisaged,includingfrommarketing,businessintelligenceandfinancialprediction.
Mining opinions and sentiments from natural language, however, is an extremely
difficulttaskasitinvolvesadeepunderstandingofmostoftheexplicitandimplicit,
regular and irregular, syntactical and semantic rules appropriate of a language.
Existing approaches to sentiment analysis mainly rely on parts of text in which
opinions are explicitly expressed such as polarity terms, affect words, and their
co-occurrence frequencies. However, opinions and sentiments are often conveyed
implicitly through latent semantics, which make purely syntactical approaches
ineffective.
Concept-levelapproaches, instead, use Web ontologiesor semantic networks to
accomplish semantic text analysis. This helps the system grasp the conceptual and
affectiveinformationassociatedwithnaturallanguageopinions.Byrelyingonlarge
semanticknowledgebases,suchapproachesstepawayfromblindlyusingkeywords
andwordco-occurrencecountsandinsteadrelyontheimplicitmeaning/featuresas-
sociated with natural language concepts. Superior to purely syntactical techniques,
concept-based approaches can detect subtly expressed sentiments. Concept-based
approaches, in fact, can analyze multi-word expressions that do not explicitly
conveyemotion,butare relatedto conceptsthatdoso.
Senticcomputingisapioneeringmulti-disciplinaryapproachtonaturallanguage
processing and understanding at the crossroads between affective computing,
information extraction, and common-sense reasoning, and exploits both computer
and human sciences to better interpret and process social information on the Web.
In sentic computing, whose term derives from the Latin “sentire” (root of words
such as sentiment and sentience) and “sensus” (as in common sense), the analysis
of natural language is based on common-sense reasoning tools, which enable the
analysis of text not only at the document, page, or paragraph level but also at the
sentence,clause,andconceptlevel.
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