# TamilEmo: Finegrained Emotion Detection Dataset for Tamil

arXiv: https://arxiv.org/abs/2202.04725 · HTML: https://mlsanity.com/paper/2202.04725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text](https://mlsanity.com/paper/2106.09460/) (2021)
- [Exploring Transformers in Emotion Recognition: a comparison of BERT, DistillBERT, RoBERTa, XLNet and ELECTRA](https://mlsanity.com/paper/2104.02041/) (2021)
- [Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition](https://mlsanity.com/paper/2108.00194/) (2021)
- [Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and Posts](https://mlsanity.com/paper/2108.10939/) (2021)

## Abstract (arXiv)

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.