DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text
This addresses the lack of resources for code-mixed text analysis in under-resourced Dravidian languages, providing a dataset for researchers and practitioners, though it is incremental as it builds on existing data collection efforts.
The paper introduces a manually annotated dataset of over 60,000 YouTube comments in three Dravidian languages (Tamil, Kannada, Malayalam) mixed with English for sentiment analysis and offensive language identification, with baseline experiments to set benchmarks.
This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7,000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff's alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning methods. The dataset is available on Github (https://github.com/bharathichezhiyan/DravidianCodeMix-Dataset) and Zenodo (https://zenodo.org/record/4750858\#.YJtw0SYo\_0M).