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