gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data

The phenomenon of mixing the vocabulary and syntax of multiple languages\nwithin the same utterance is called Code-Mixing. This is more evident in\nmultilingual societies. In this paper, we have developed a system for SemEval\n2020: Task 9 on Sentiment Analysis for Code-Mixed Social Media Text. Our system\nfirst generates two types of embeddings for the social media text. In those,\nthe first one is character level embeddings to encode the character level\ninformation and to handle the out-of-vocabulary entries and the second one is\nFastText word embeddings for capturing morphology and semantics. These two\nembeddings were passed to the LSTM network and the system outperformed the\nbaseline model.\n