@inproceedings{b-etal-2022-findings,
title = "Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in {D}ravidian Languages",
author = "B, Premjith and
Chakravarthi, Bharathi Raja and
Subramanian, Malliga and
B, Bharathi and
Kp, Soman and
V, Dhanalakshmi and
K, Sreelakshmi and
Pandian, Arunaggiri and
Kumaresan, Prasanna",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.39",
doi = "10.18653/v1/2022.dravidianlangtech-1.39",
pages = "254--260",
abstract = "This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.",
}
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<abstract>This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.</abstract>
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%0 Conference Proceedings
%T Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages
%A B, Premjith
%A Chakravarthi, Bharathi Raja
%A Subramanian, Malliga
%A B, Bharathi
%A Kp, Soman
%A V, Dhanalakshmi
%A K, Sreelakshmi
%A Pandian, Arunaggiri
%A Kumaresan, Prasanna
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F b-etal-2022-findings
%X This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.
%R 10.18653/v1/2022.dravidianlangtech-1.39
%U https://aclanthology.org/2022.dravidianlangtech-1.39
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.39
%P 254-260
Markdown (Informal)
[Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages](https://aclanthology.org/2022.dravidianlangtech-1.39) (B et al., DravidianLangTech 2022)
ACL
- Premjith B, Bharathi Raja Chakravarthi, Malliga Subramanian, Bharathi B, Soman Kp, Dhanalakshmi V, Sreelakshmi K, Arunaggiri Pandian, and Prasanna Kumaresan. 2022. Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 254–260, Dublin, Ireland. Association for Computational Linguistics.