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Sentiment analysis has been an active area of research in the past twodecades and recently, with the advent of social media, there has been anincreasing demand for sentiment analysis on social media texts. Since thesocial media texts are not in one language and are largely code-mixed innature, the traditional sentiment classification models fail to produceacceptable results. This paper tries to solve this very research problem anduses bi-directional LSTMs along with language tagging, to facilitate sentimenttagging of code-mixed Tamil texts that have been extracted from social media.The presented algorithm, when evaluated on the test data, garnered precision,recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.

JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags