World Wide Web

Natural language

Linguistics

Philosophy

Biology

Discriminative model

Identification (biology)

Language identification

Economics

Set (abstract data type)

Computer science

Botany

Code (set theory)

Geology

Oceanography

Natural language processing

Malayalam

Management

Social media

Offensive

Artificial intelligence

Information retrieval

Programming language

Tamil

Sentiment analysis

Baseline (sea)

Language Resources and Evaluation

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).

DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text