Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23766
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dc.contributor.authorPoria, Soujanya-
dc.contributor.authorCambria, Erik-
dc.contributor.authorGelbukh, Alexander-
dc.contributor.authorBisio, Federica-
dc.contributor.authorHussain, Amir-
dc.date.accessioned2016-07-14T00:03:31Z-
dc.date.available2016-07-14T00:03:31Z-
dc.date.issued2015-11-
dc.identifier.urihttp://hdl.handle.net/1893/23766-
dc.description.abstractEmulating the human brain is one of the core challenges of computational intelligence, which entails many key problems of artificial intelligence, including understanding human language, reasoning, and emotions. In this work, computational intelligence techniques are combined with common-sense computing and linguistics to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive tasks such as polarity detection from big social data. The main novelty of the paper consists in an algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence. Analyzing how sentiment flows from concept to concept through dependency relations allows for a better understanding of the contextual role of each concept in text, to achieve a dynamic polarity inference that outperforms state-of-the- art statistical methods in terms of both accuracy and training time.en_UK
dc.language.isoen-
dc.publisherIEEE-
dc.relationPoria S, Cambria E, Gelbukh A, Bisio F & Hussain A (2015) Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns, IEEE Computational Intelligence Magazine, 10 (4), pp. 26-36.-
dc.rightsc) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.-
dc.subjectBig Dataen_UK
dc.subjectInterneten_UK
dc.subjectcomputational linguisticsen_UK
dc.subjectdata flow analysisen_UK
dc.subjectdata structuresen_UK
dc.subjectnatural languagesen_UK
dc.subjectsocial networking (online)en_UK
dc.subjecttext analysisen_UK
dc.subjectBehavioral scienceen_UK
dc.subjectBiological system modelingen_UK
dc.subjectElectronic circuitsen_UK
dc.subjectKnowledge based systemsen_UK
dc.subjectLearning systemsen_UK
dc.subjectLinguisticsen_UK
dc.subjectPragmaticsen_UK
dc.subjectSemanticsen_UK
dc.subjectSentiment analysisen_UK
dc.subjectartificial intelligenceen_UK
dc.subjectbig social dataen_UK
dc.subjectcommon-sense computingen_UK
dc.subjectcomputational intelligence techniqueen_UK
dc.subjectdynamic linguistic patternen_UK
dc.subjectdynamic polarity inferenceen_UK
dc.subjecthuman brainen_UK
dc.subjectnatural language texten_UK
dc.subjectsentiment data flow analysisen_UK
dc.subjectsocial media marketingen_UK
dc.subjectstatistical methodsen_UK
dc.subjectunstructured dataen_UK
dc.subjectword-level text analysisen_UK
dc.titleSentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patternsen_UK
dc.typeJournal Articleen_UK
dc.identifier.doihttp://dx.doi.org/10.1109/MCI.2015.2471215-
dc.citation.jtitleIEEE Computational Intelligence Magazine-
dc.citation.issn1556-603X-
dc.citation.volume10-
dc.citation.issue4-
dc.citation.spage26-
dc.citation.epage36-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPost-print (author final draft post-refereeing)-
dc.author.emailahu@cs.stir.ac.uk-
dc.citation.date12/10/2015-
dc.contributor.affiliationUniversity of Stirling-
dc.contributor.affiliationNanyang Technological University-
dc.contributor.affiliationInstituto Polit├ęcnico Nacional-
dc.contributor.affiliationUniversity of Genoa-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.identifier.isi000363206100004-
Appears in Collections:Computing Science and Mathematics Journal Articles

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