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Code-mixing is the phenomenon of using multiple languages in the sameutterance of a text or speech. It is a frequently used pattern of communicationon various platforms such as social media sites, online gaming, productreviews, etc. Sentiment analysis of the monolingual text is a well-studiedtask. Code-mixing adds to the challenge of analyzing the sentiment of the textdue to the non-standard writing style. We present a candidate sentencegeneration and selection based approach on top of the Bi-LSTM based neuralclassifier to classify the Hinglish code-mixed text into one of the threesentiment classes positive, negative, or neutral. The proposed approach showsan improvement in the system performance as compared to the Bi-LSTM basedneural classifier. The results present an opportunity to understand variousother nuances of code-mixing in the textual data, such as humor-detection,intent classification, etc.

IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection