  Emotional Analysis from textual input has been considered both a challenging
and interesting task in Natural Language Processing. However, due to the lack
of datasets in low-resource languages (i.e. Tamil), it is difficult to conduct
research of high standard in this area. Therefore we introduce this labelled
dataset (a largest manually annotated dataset of more than 42k Tamil YouTube
comments, labelled for 31 emotions including neutral) for emotion recognition.
The goal of this dataset is to improve emotion detection in multiple downstream
tasks in Tamil. We have also created three different groupings of our emotions
(3-class, 7-class and 31-class) and evaluated the model's performance on each
category of the grouping. Our MURIL-base model has achieved a 0.60 macro
average F1-score across our 3-class group dataset. With 7-class and 31-class
groups, the Random Forest model performed well with a macro average F1-scores
of 0.42 and 0.29 respectively.
