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Code-mixing is a phenomenon which arises mainly in multilingual societies.Multilingual people, who are well versed in their native languages and alsoEnglish speakers, tend to code-mix using English-based phonetic typing and theinsertion of anglicisms in their main language. This linguistic phenomenonposes a great challenge to conventional NLP domains such as Sentiment Analysis,Machine Translation, and Text Summarization, to name a few. In this work, wefocus on working out a plausible solution to the domain of Code-Mixed SentimentAnalysis. This work was done as participation in the SemEval-2020 SentimixTask, where we focused on the sentiment analysis of English-Hindi code-mixedsentences. our username for the submission was "sainik.mahata" and team namewas "JUNLP". We used feature extraction algorithms in conjunction withtraditional machine learning algorithms such as SVR and Grid Search in anattempt to solve the task. Our approach garnered an f1-score of 66.2\% whentested using metrics prepared by the organizers of the task.

JUNLP@SemEval-2020 Task 9:Sentiment Analysis of Hindi-English code mixed data using Grid Search Cross Validation