Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network

Multimodal sentiment analysis has currently identified its significance in a variety of domains. For the purpose of sentiment analysis, different aspects of distinguishing modalities, which correspond to one target, are processed and analyzed. In this work, the researchers propose the targeted aspec...

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Autores principales:	Donghong Gu, Jiaqian Wang, Shaohua Cai, Chi Yang, Zhengxin Song, Haoliang Zhao, Luwei Xiao, Hua Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Multimodal sentiment analysis textual and visual modalities feature extraction multimodality fusion Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b2162184dd094c02b4e0b2dd6401227a
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id	oai:doaj.org-article:b2162184dd094c02b4e0b2dd6401227a
record_format	dspace
spelling	oai:doaj.org-article:b2162184dd094c02b4e0b2dd6401227a2021-12-03T00:00:24ZTargeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network2169-353610.1109/ACCESS.2021.3126782https://doaj.org/article/b2162184dd094c02b4e0b2dd6401227a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606882/https://doaj.org/toc/2169-3536Multimodal sentiment analysis has currently identified its significance in a variety of domains. For the purpose of sentiment analysis, different aspects of distinguishing modalities, which correspond to one target, are processed and analyzed. In this work, the researchers propose the targeted aspect-based multimodal sentiment analysis (TABMSA) for the first time. Furthermore, an attention capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA is devised. The multi-head attention (MHA) based network and the ResNet-152 are employed to deal with texts and images, respectively. The integration of MHA and capsule network aims to capture the interaction among the multimodal inputs. In addition to the targeted aspect, the information from the context and the image is also incorporated for sentiment delivered. The researchers evaluate the proposed model on two manually annotated datasets. the experimental results demonstrate the effectiveness of our proposed model for this new task.Donghong GuJiaqian WangShaohua CaiChi YangZhengxin SongHaoliang ZhaoLuwei XiaoHua WangIEEEarticleMultimodal sentiment analysistextual and visual modalitiesfeature extractionmultimodality fusionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157329-157336 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multimodal sentiment analysis textual and visual modalities feature extraction multimodality fusion Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Multimodal sentiment analysis textual and visual modalities feature extraction multimodality fusion Electrical engineering. Electronics. Nuclear engineering TK1-9971 Donghong Gu Jiaqian Wang Shaohua Cai Chi Yang Zhengxin Song Haoliang Zhao Luwei Xiao Hua Wang Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
description	Multimodal sentiment analysis has currently identified its significance in a variety of domains. For the purpose of sentiment analysis, different aspects of distinguishing modalities, which correspond to one target, are processed and analyzed. In this work, the researchers propose the targeted aspect-based multimodal sentiment analysis (TABMSA) for the first time. Furthermore, an attention capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA is devised. The multi-head attention (MHA) based network and the ResNet-152 are employed to deal with texts and images, respectively. The integration of MHA and capsule network aims to capture the interaction among the multimodal inputs. In addition to the targeted aspect, the information from the context and the image is also incorporated for sentiment delivered. The researchers evaluate the proposed model on two manually annotated datasets. the experimental results demonstrate the effectiveness of our proposed model for this new task.
format	article
author	Donghong Gu Jiaqian Wang Shaohua Cai Chi Yang Zhengxin Song Haoliang Zhao Luwei Xiao Hua Wang
author_facet	Donghong Gu Jiaqian Wang Shaohua Cai Chi Yang Zhengxin Song Haoliang Zhao Luwei Xiao Hua Wang
author_sort	Donghong Gu
title	Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
title_short	Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
title_full	Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
title_fullStr	Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
title_full_unstemmed	Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
title_sort	targeted aspect-based multimodal sentiment analysis: an attention capsule extraction and multi-head fusion network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b2162184dd094c02b4e0b2dd6401227a
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Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network

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