Publication
A Multimodal Approach to Image Sentiment Analysis
| dc.contributor.author | Gaspar, António | |
| dc.contributor.author | Alexandre, Luís | |
| dc.date.accessioned | 2020-01-09T12:30:00Z | |
| dc.date.available | 2020-01-09T12:30:00Z | |
| dc.date.issued | 2019-11 | |
| dc.description.abstract | Multimodal sentiment analysis is a process for the classi- cation of the content of composite comments in social media at the sentiment level that takes into consideration not just the textual content but also the accompanying images. A composite comment is normally represented by the union of text and image. Multimodal sentiment analysis has a great dependency on text to obtain its classi cation, because image analysis can be very subjective according to the context where the image is inserted. In this paper we propose a method that reduces the text analysis dependency on this kind of classi cation giving more importance to the image content. Our method is divided into three main parts: a text analysis method that was adapted to the task, an image classi er tuned with the dataset that we use, and a method that analyses the class content of an image and checks the probability that it belongs to one of the possible classes. Finally a weighted sum takes the results of these methods into account to classify content according to its sentiment class. We improved the accuracy on the dataset used by more than 9%. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.6/8162 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.subject | Multimodal Sentiment Analysis | pt_PT |
| dc.subject | Image | pt_PT |
| dc.subject | Text | pt_PT |
| dc.subject | Deep Learning | pt_PT |
| dc.title | A Multimodal Approach to Image Sentiment Analysis | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.title | 20th International Conference on Intelligent Data Engineering and Automated Learning | pt_PT |
| person.familyName | Gaspar | |
| person.familyName | Alexandre | |
| person.givenName | António | |
| person.givenName | Luís | |
| person.identifier.ciencia-id | CD18-EA5A-2ECC | |
| person.identifier.ciencia-id | 2014-0F06-A3E3 | |
| person.identifier.orcid | 0000-0002-6354-3374 | |
| person.identifier.orcid | 0000-0002-5133-5025 | |
| person.identifier.rid | E-8770-2013 | |
| person.identifier.scopus-author-id | 8847713100 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 968e425b-592e-4f24-a1a4-d310db5c5e68 | |
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| relation.isAuthorOfPublication.latestForDiscovery | 968e425b-592e-4f24-a1a4-d310db5c5e68 |