@inproceedings{b-etal-2023-findings,
title = "Findings of the Shared Task on Multimodal Abusive Language Detection and Sentiment Analysis in {T}amil and {M}alayalam",
author = "B, Premjith and
G, Jyothish Lal and
V, Sowmya and
Chakravarthi, Bharathi Raja and
Natarajan, Rajeswari and
K, Nandhini and
Murugappan, Abirami and
B, Bharathi and
M, Kaushik and
Sn, Prasanth and
R, Aswin Raj and
S, Vijai Simmon",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.10",
pages = "72--79",
abstract = "This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score.",
}
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<abstract>This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score.</abstract>
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%0 Conference Proceedings
%T Findings of the Shared Task on Multimodal Abusive Language Detection and Sentiment Analysis in Tamil and Malayalam
%A B, Premjith
%A G, Jyothish Lal
%A V, Sowmya
%A Chakravarthi, Bharathi Raja
%A Natarajan, Rajeswari
%A K, Nandhini
%A Murugappan, Abirami
%A B, Bharathi
%A M, Kaushik
%A Sn, Prasanth
%A R, Aswin Raj
%A S, Vijai Simmon
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F b-etal-2023-findings
%X This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score.
%U https://aclanthology.org/2023.dravidianlangtech-1.10
%P 72-79
Markdown (Informal)
[Findings of the Shared Task on Multimodal Abusive Language Detection and Sentiment Analysis in Tamil and Malayalam](https://aclanthology.org/2023.dravidianlangtech-1.10) (B et al., DravidianLangTech-WS 2023)
ACL
- Premjith B, Jyothish Lal G, Sowmya V, Bharathi Raja Chakravarthi, Rajeswari Natarajan, Nandhini K, Abirami Murugappan, Bharathi B, Kaushik M, Prasanth Sn, Aswin Raj R, and Vijai Simmon S. 2023. Findings of the Shared Task on Multimodal Abusive Language Detection and Sentiment Analysis in Tamil and Malayalam. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 72–79, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.