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With the popularity of social media, communications through blogs, Facebook,Twitter, and other plat-forms have increased. Initially, English was the onlymedium of communication. Fortunately, now we can communicate in any language.It has led to people using English and their own native or mother tonguelanguage in a mixed form. Sometimes, comments in other languages have Englishtransliterated format or other cases; people use the intended language scripts.Identifying sentiments and offensive content from such code mixed tweets is anecessary task in these times. We present a working model submitted for Task2of the sub-track HASOC Offensive Language Identification- DravidianCodeMix inForum for Information Retrieval Evaluation, 2020. It is a message levelclassification task. An embedding model-based classifier identifies offensiveand not offensive comments in our approach. We applied this method in theManglish dataset provided along with the sub-track.

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