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Code-mixing(CM) is a frequently observed phenomenon that uses multiplelanguages in an utterance or sentence. CM is mostly practiced on various socialmedia platforms and in informal conversations. Sentiment analysis (SA) is afundamental step in NLP and is well studied in the monolingual text.Code-mixing adds a challenge to sentiment analysis due to its non-standardrepresentations. This paper proposes a meta embedding with a transformer methodfor sentiment analysis on the Dravidian code-mixed dataset. In our method, weused meta embeddings to capture rich text representations. We used the proposedmethod for the Task: "Sentiment Analysis for Dravidian Languages in Code-MixedText", and it achieved an F1 score of $0.58$ and $0.66$ for the given Dravidiancode mixed data sets. The code is provided in the Githubhttps://github.com/suman101112/fire-2020-Dravidian-CodeMix.

CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text