CUSATNLP@HASOC-Dravidian-CodeMix-FIRE2020:Identifying Offensive Language from ManglishTweets

TL;DR

This paper presents a model for identifying offensive language in code-mixed Manglish tweets, addressing the challenge of mixed-language social media content using embedding-based classification.

Contribution

It introduces a novel approach applying embedding models to classify offensive content in code-mixed Dravidian language tweets, specifically for the HASOC-Dravidian-CodeMix task.

Findings

Effective identification of offensive language in Manglish tweets

Demonstrated the utility of embedding models for code-mixed language classification

Achieved competitive results in the HASOC-Dravidian-CodeMix task

Abstract

With the popularity of social media, communications through blogs, Facebook, Twitter, and other plat-forms have increased. Initially, English was the only medium of communication. Fortunately, now we can communicate in any language. It has led to people using English and their own native or mother tongue language in a mixed form. Sometimes, comments in other languages have English transliterated format or other cases; people use the intended language scripts. Identifying sentiments and offensive content from such code mixed tweets is a necessary task in these times. We present a working model submitted for Task2 of the sub-track HASOC Offensive Language Identification- DravidianCodeMix in Forum for Information Retrieval Evaluation, 2020. It is a message level classification task. An embedding model-based classifier identifies offensive and not offensive comments in our approach. We applied this method in the Manglish dataset provided along with the sub-track.