DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam
This addresses the lack of multimodal resources for Dravidian languages, enabling sentiment analysis research in these under-resourced contexts.
The researchers created the first multimodal sentiment analysis dataset for the under-resourced Tamil and Malayalam languages by collecting YouTube review videos, adding captions, and labeling sentiment with verified inter-annotator agreement.
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.