# DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam

arXiv: https://arxiv.org/abs/2106.04853 · HTML: https://mlsanity.com/paper/2106.04853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [Low Rank Fusion based Transformers for Multimodal Sequences](https://mlsanity.com/paper/2007.02038/) (2020)
- [Benchmarking Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.09538/) (2017)
- [Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning](https://mlsanity.com/paper/1909.02764/) (2019)
- [Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities](https://mlsanity.com/paper/1807.01122/) (2018)

## Built on by (most cited first, 4 of 4)

- [Large Multimodal Models for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2502.05568/) (2025)
- [Pretrained Transformers for Offensive Language Identification in Tanglish](https://mlsanity.com/paper/2110.02852/) (2021)
- [Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm](https://mlsanity.com/paper/2205.13148/) (2022)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)

## Abstract (arXiv)

Human communication is inherently multimodal and asynchronous. Analyzing human emotions and sentiment is an emerging field of artificial intelligence. We are witnessing an increasing amount of multimodal content in local languages on social media about products and other topics. However, there are not many multimodal resources available for under-resourced Dravidian languages. Our study aims to create a multimodal sentiment analysis dataset for the under-resourced Tamil and Malayalam languages. First, we downloaded product or movies review videos from YouTube for Tamil and Malayalam. Next, we created captions for the videos with the help of annotators. Then we labelled the videos for sentiment, and verified the inter-annotator agreement using Fleiss's Kappa. This is the first multimodal sentiment analysis dataset for Tamil and Malayalam by volunteer annotators.