Sentiment Analysis (SA) in Dravidian codemixed text is a hot research area right now. In this regard, the “Second Shared Task on SA in Code-mixed Tamil and Tulu” at Dravidian- LangTech (EACL-2024) is organized. Two tasks namely SA in Tamil-English and Tulu- English code-mixed data, make up this shared assignment. In total, 64 teams registered for the shared task, out of which 19 and 17 systems were received for Tamil and Tulu, respectively. The performance of the systems submitted by the participants was evaluated based on the macro F1-score. The best method obtained macro F1-scores of 0.260 and 0.584 for code-mixed Tamil and Tulu texts, respectively.

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.