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

TL;DR

This paper introduces a large, manually annotated dataset for sentiment analysis and offensive language detection in code-mixed social media comments across three under-resourced Dravidian languages, providing benchmarks for future research.

Contribution

The paper presents a new multilingual, code-mixed dataset for Dravidian languages with high-quality annotations and baseline machine learning experiments, addressing resource scarcity.

Findings

High inter-annotator agreement in annotations

Dataset includes diverse code-mixing phenomena

Baseline models establish initial benchmarks

Abstract

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).