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

Code-mixing is a phenomenon which arises mainly in multilingual societies.\nMultilingual people, who are well versed in their native languages and also\nEnglish speakers, tend to code-mix using English-based phonetic typing and the\ninsertion of anglicisms in their main language. This linguistic phenomenon\nposes a great challenge to conventional NLP domains such as Sentiment Analysis,\nMachine Translation, and Text Summarization, to name a few. In this work, we\nfocus on working out a plausible solution to the domain of Code-Mixed Sentiment\nAnalysis. This work was done as participation in the SemEval-2020 Sentimix\nTask, where we focused on the sentiment analysis of English-Hindi code-mixed\nsentences. our username for the submission was "sainik.mahata" and team name\nwas "JUNLP". We used feature extraction algorithms in conjunction with\ntraditional machine learning algorithms such as SVR and Grid Search in an\nattempt to solve the task. Our approach garnered an f1-score of 66.2\\% when\ntested using metrics prepared by the organizers of the task.\n