@inproceedings{hegde-etal-2023-findings,
title = "Findings of the Shared Task on Sentiment Analysis in {T}amil and {T}ulu Code-Mixed Text",
author = "Hegde, Asha and
Chakravarthi, Bharathi Raja and
Shashirekha, Hosahalli Lakshmaiah and
Ponnusamy, Rahul and
Cn, Subalalitha and
S K, Lavanya and
D., Thenmozhi and
Karunakar, Martha and
Shreeram, Shreya and
Aymen, Sarah",
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-1.9",
pages = "64--71",
abstract = "In recent years, there has been a growing focus on Sentiment Analysis (SA) of code-mixed Dravidian languages. However, the majority of social media text in these languages is code-mixed, presenting a unique challenge. Despite this, there is currently lack of research on SA specifically tailored for code-mixed Dravidian languages, highlighting the need for further exploration and development in this domain. In this view, {``}Sentiment Analysis in Tamil and Tulu- DravidianLangTech{''} shared task at Recent Advances in Natural Language Processing (RANLP)- 2023 is organized. This shred consists two language tracks: code-mixed Tamil and Tulu and Tulu text is first ever explored in public domain for SA. We describe the task, its organization, and the submitted systems followed by the results. 57 research teams registered for the shared task and We received 27 systems each for code-mixed Tamil and Tulu texts. The performance of the systems (developed by participants) has been evaluated in terms of macro average F1 score. The top system for code-mixed Tamil and Tulu texts scored macro average F1 score of 0.32, and 0.542 respectively. The high quality and substantial quantity of submissions demonstrate a significant interest and attention in the analysis of code-mixed Dravidian languages. However, the current state of the art in this domain indicates the need for further advancements and improvements to effectively address the challenges posed by code-mixed Dravidian language SA.",
}
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<abstract>In recent years, there has been a growing focus on Sentiment Analysis (SA) of code-mixed Dravidian languages. However, the majority of social media text in these languages is code-mixed, presenting a unique challenge. Despite this, there is currently lack of research on SA specifically tailored for code-mixed Dravidian languages, highlighting the need for further exploration and development in this domain. In this view, “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)- 2023 is organized. This shred consists two language tracks: code-mixed Tamil and Tulu and Tulu text is first ever explored in public domain for SA. We describe the task, its organization, and the submitted systems followed by the results. 57 research teams registered for the shared task and We received 27 systems each for code-mixed Tamil and Tulu texts. The performance of the systems (developed by participants) has been evaluated in terms of macro average F1 score. The top system for code-mixed Tamil and Tulu texts scored macro average F1 score of 0.32, and 0.542 respectively. The high quality and substantial quantity of submissions demonstrate a significant interest and attention in the analysis of code-mixed Dravidian languages. However, the current state of the art in this domain indicates the need for further advancements and improvements to effectively address the challenges posed by code-mixed Dravidian language SA.</abstract>
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%0 Conference Proceedings
%T Findings of the Shared Task on Sentiment Analysis in Tamil and Tulu Code-Mixed Text
%A Hegde, Asha
%A Chakravarthi, Bharathi Raja
%A Shashirekha, Hosahalli Lakshmaiah
%A Ponnusamy, Rahul
%A Cn, Subalalitha
%A S K, Lavanya
%A D., Thenmozhi
%A Karunakar, Martha
%A Shreeram, Shreya
%A Aymen, Sarah
%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 hegde-etal-2023-findings
%X In recent years, there has been a growing focus on Sentiment Analysis (SA) of code-mixed Dravidian languages. However, the majority of social media text in these languages is code-mixed, presenting a unique challenge. Despite this, there is currently lack of research on SA specifically tailored for code-mixed Dravidian languages, highlighting the need for further exploration and development in this domain. In this view, “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)- 2023 is organized. This shred consists two language tracks: code-mixed Tamil and Tulu and Tulu text is first ever explored in public domain for SA. We describe the task, its organization, and the submitted systems followed by the results. 57 research teams registered for the shared task and We received 27 systems each for code-mixed Tamil and Tulu texts. The performance of the systems (developed by participants) has been evaluated in terms of macro average F1 score. The top system for code-mixed Tamil and Tulu texts scored macro average F1 score of 0.32, and 0.542 respectively. The high quality and substantial quantity of submissions demonstrate a significant interest and attention in the analysis of code-mixed Dravidian languages. However, the current state of the art in this domain indicates the need for further advancements and improvements to effectively address the challenges posed by code-mixed Dravidian language SA.
%U https://aclanthology.org/2023.dravidianlangtech-1.9
%P 64-71
Markdown (Informal)
[Findings of the Shared Task on Sentiment Analysis in Tamil and Tulu Code-Mixed Text](https://aclanthology.org/2023.dravidianlangtech-1.9) (Hegde et al., DravidianLangTech-WS 2023)
ACL
- Asha Hegde, Bharathi Raja Chakravarthi, Hosahalli Lakshmaiah Shashirekha, Rahul Ponnusamy, Subalalitha Cn, Lavanya S K, Thenmozhi D., Martha Karunakar, Shreya Shreeram, and Sarah Aymen. 2023. Findings of the Shared Task on Sentiment Analysis in Tamil and Tulu Code-Mixed Text. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 64–71, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.