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