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. If you are using the data or code from this research then please site our paper below: @article{chakravarthi-etal-2021-lre, title = "DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text", author = "Chakravarthi, Bharathi Raja and Priyadharshini, Ruba and Muralidaran, Vigneshwaran and Jose, Navya and Suryawanshi, Shardul and Sherly, Elizabeth and McCrae, John P journal={Language Resources and Evaluation}, year={2021}, publisher={Springer} }