@inproceedings{mao-etal-2022-sena,
title = "{M}-{SENA}: An Integrated Platform for Multimodal Sentiment Analysis",
author = "Mao, Huisheng and
Yuan, Ziqi and
Xu, Hua and
Yu, Wenmeng and
Liu, Yihe and
Gao, Kai",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.20",
doi = "10.18653/v1/2022.acl-demo.20",
pages = "204--213",
abstract = "M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at \url{https://github.com/thuiar/M-SENA}.",
}
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<abstract>M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at https://github.com/thuiar/M-SENA.</abstract>
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%0 Conference Proceedings
%T M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
%A Mao, Huisheng
%A Yuan, Ziqi
%A Xu, Hua
%A Yu, Wenmeng
%A Liu, Yihe
%A Gao, Kai
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mao-etal-2022-sena
%X M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at https://github.com/thuiar/M-SENA.
%R 10.18653/v1/2022.acl-demo.20
%U https://aclanthology.org/2022.acl-demo.20
%U https://doi.org/10.18653/v1/2022.acl-demo.20
%P 204-213
Markdown (Informal)
[M-SENA: An Integrated Platform for Multimodal Sentiment Analysis](https://aclanthology.org/2022.acl-demo.20) (Mao et al., ACL 2022)
ACL
- Huisheng Mao, Ziqi Yuan, Hua Xu, Wenmeng Yu, Yihe Liu, and Kai Gao. 2022. M-SENA: An Integrated Platform for Multimodal Sentiment Analysis. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 204–213, Dublin, Ireland. Association for Computational Linguistics.