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dc.contributor.authorPoria, Soujanyaen_UK
dc.contributor.authorCambria, Eriken_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.authorHuang, Guang-Binen_UK
dc.description.abstractAn increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.en_UK
dc.relationPoria S, Cambria E, Hussain A & Huang G (2015) Towards an intelligent framework for multimodal affective data analysis. Neural Networks, 63, pp. 104-116.
dc.rightsPublished in Neural Networks by Elsevier; Elsevier believes that individual authors should be able to distribute their AAMs for their personal voluntary needs and interests, e.g. posting to their websites or their institution’s repository, e-mailing to colleagues. However, our policies differ regarding the systematic aggregation or distribution of AAMs to ensure the sustainability of the journals to which AAMs are submitted. Therefore, deposit in, or posting to, subject-oriented or centralized repositories (such as PubMed Central), or institutional repositories with systematic posting mandates is permitted only under specific agreements between Elsevier and the repository, agency or institution, and only consistent with the publisher’s policies concerning such repositories. Voluntary posting of AAMs in the arXiv subject repository is permitted.en_UK
dc.subjectMultimodal sentiment analysisen_UK
dc.subjectFacial expressionsen_UK
dc.subjectEmotion analysisen_UK
dc.subjectAffective computingen_UK
dc.titleTowards an intelligent framework for multimodal affective data analysisen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleNeural Networksen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationNanyang Technological Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationNanyang Technological Universityen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorPoria, Soujanya|en_UK
local.rioxx.authorCambria, Erik|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.authorHuang, Guang-Bin|en_UK
local.rioxx.projectInternal Project|University of Stirling|
local.rioxx.filenameNeural Networks 2014.pdfen_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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