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Findings of the Shared Task on Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL)

Publication Type : Conference Paper

Publisher : Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

Source : Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 56–61, St. Julian's, Malta. Association for Computational Linguistics

Url : https://aclanthology.org/2024.dravidianlangtech-1.9.pdf

Campus : Coimbatore

School : School of Artificial Intelligence

Year : 2024

Abstract : This paper presents the findings of the shared task on multimodal sentiment analysis, abusive language detection and hate speech detection in Dravidian languages. Through this shared task, researchers worldwide can submit models for three crucial social media data analysis challenges in Dravidian languages: sentiment analysis, abusive language detection, and hate speech detection. The aim is to build models for deriving fine-grained sentiment analysis from multimodal data in Tamil and Malayalam, identifying abusive and hate content from multimodal data in Tamil. Three modalities make up the multimodal data: text, audio, and video. YouTube videos were gathered to create the datasets for the tasks. Thirty-nine teams took part in the competition. However, only two teams, though, turned in their findings. The macro F1-score was used to assess the submissions.

Cite this Research Publication : Premjith B, Jyothish G, Sowmya V, Bharathi Raja Chakravarthi, K Nandhini, Rajeswari Natarajan, Abirami Murugappan, Bharathi B, Saranya Rajiakodi, Rahul Ponnusamy, Jayanth Mohan, and Mekapati Reddy. 2024. Findings of the Shared Task on Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL)@DravidianLangTech 2024. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 56–61, St. Julian's, Malta. Association for Computational Linguistics.

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