  The phenomenon of mixing the vocabulary and syntax of multiple languages
within the same utterance is called Code-Mixing. This is more evident in
multilingual societies. In this paper, we have developed a system for SemEval
2020: Task 9 on Sentiment Analysis for Code-Mixed Social Media Text. Our system
first generates two types of embeddings for the social media text. In those,
the first one is character level embeddings to encode the character level
information and to handle the out-of-vocabulary entries and the second one is
FastText word embeddings for capturing morphology and semantics. These two
embeddings were passed to the LSTM network and the system outperformed the
baseline model.
