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This paper describes the development of a multilingual, manually annotateddataset for three under-resourced Dravidian languages generated from socialmedia comments. The dataset was annotated for sentiment analysis and offensivelanguage identification for a total of more than 60,000 YouTube comments. Thedataset consists of around 44,000 comments in Tamil-English, around 7,000comments in Kannada-English, and around 20,000 comments in Malayalam-English.The data was manually annotated by volunteer annotators and has a highinter-annotator agreement in Krippendorff's alpha. The dataset contains alltypes of code-mixing phenomena since it comprises user-generated content from amultilingual country. We also present baseline experiments to establishbenchmarks on the dataset using machine learning methods. The dataset isavailable on Github(https://github.com/bharathichezhiyan/DravidianCodeMix-Dataset) and Zenodo(https://zenodo.org/record/4750858\#.YJtw0SYo\_0M).

DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text