Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25317
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPoria, Soujanyaen_UK
dc.contributor.authorChaturvedi, Itien_UK
dc.contributor.authorCambria, Eriken_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.editorBonchi, Fen_UK
dc.contributor.editorDomingo-Ferrer, Jen_UK
dc.contributor.editorBaeza-Yates, Ren_UK
dc.contributor.editorZhou, Z-Hen_UK
dc.contributor.editorWu, Xen_UK
dc.date.accessioned2017-06-26T22:54:40Z-
dc.date.available2017-06-26T22:54:40Z-
dc.date.issued2017-02-02en_UK
dc.identifier.urihttp://hdl.handle.net/1893/25317-
dc.description.abstractTechnology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets.en_UK
dc.language.isoenen_UK
dc.publisherIEEE Computer Societyen_UK
dc.relationPoria S, Chaturvedi I, Cambria E & Hussain A (2017) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: Bonchi F, Domingo-Ferrer J, Baeza-Yates R, Zhou Z & Wu X (eds.) Proceedings - IEEE 16th International Conference on Data Mining, ICDM 2016. 2016 IEEE 16th International Conference on Data Mining, Barcelona, Spain, 12.12.2016-15.12.2016. Los Alamitos, CA, USA: IEEE Computer Society, pp. 439-448. https://doi.org/10.1109/ICDM.2016.178en_UK
dc.rights© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.en_UK
dc.subjectSentiment analysisen_UK
dc.subjectKernelen_UK
dc.subjectNeuronsen_UK
dc.subjectEmotion recognitionen_UK
dc.subjectFeature extractionen_UK
dc.subjectBiological neural networksen_UK
dc.subjectVisualizationen_UK
dc.titleConvolutional MKL based multimodal emotion recognition and sentiment analysisen_UK
dc.typeConference Paperen_UK
dc.identifier.doi10.1109/ICDM.2016.178en_UK
dc.citation.issn2374-8486en_UK
dc.citation.spage439en_UK
dc.citation.epage448en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailamir.hussain@stir.ac.uken_UK
dc.citation.btitleProceedings - IEEE 16th International Conference on Data Mining, ICDM 2016en_UK
dc.citation.conferencedates2016-12-12 - 2016-12-15en_UK
dc.citation.conferencelocationBarcelona, Spainen_UK
dc.citation.conferencename2016 IEEE 16th International Conference on Data Miningen_UK
dc.citation.date31/12/2016en_UK
dc.citation.isbn978-1-5090-5472-5en_UK
dc.citation.isbn978-1-5090-5473-2en_UK
dc.publisher.addressLos Alamitos, CA, USAen_UK
dc.contributor.affiliationNanyang Technological Universityen_UK
dc.contributor.affiliationNanyang Technological Universityen_UK
dc.contributor.affiliationNanyang Technological Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000401811600045en_UK
dc.identifier.scopusid2-s2.0-85014552954en_UK
dc.identifier.wtid533088en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2016-09-09en_UK
dcterms.dateAccepted2016-09-09en_UK
dc.date.filedepositdate2017-05-08en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionAMen_UK
local.rioxx.authorPoria, Soujanya|en_UK
local.rioxx.authorChaturvedi, Iti|en_UK
local.rioxx.authorCambria, Erik|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.contributorBonchi, F|en_UK
local.rioxx.contributorDomingo-Ferrer, J|en_UK
local.rioxx.contributorBaeza-Yates, R|en_UK
local.rioxx.contributorZhou, Z-H|en_UK
local.rioxx.contributorWu, X|en_UK
local.rioxx.freetoreaddate2017-05-08en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2017-05-08|en_UK
local.rioxx.filenameconvolutional-mkl-based-mulimodal-sentiment-analysis.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source978-1-5090-5473-2en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

Files in This Item:
File Description SizeFormat 
convolutional-mkl-based-mulimodal-sentiment-analysis.pdfFulltext - Accepted Version530.7 kBAdobe PDFView/Open


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.