  Multilingual speakers often switch between languages to express themselves on
social communication platforms. Sometimes, the original script of the language
is preserved, while using a common script for all the languages is quite
popular as well due to convenience. On such occasions, multiple languages are
being mixed with different rules of grammar, using the same script which makes
it a challenging task for natural language processing even in case of accurate
sentiment identification. In this paper, we report results of various
experiments carried out on movie reviews dataset having this code-mixing
property of two languages, English and Bengali, both typed in Roman script. We
have tested various machine learning algorithms trained only on English
features on our code-mixed data and have achieved the maximum accuracy of
59.00% using Naive Bayes (NB) model. We have also tested various models trained
on code-mixed data, as well as English features and the highest accuracy of
72.50% was obtained by a Support Vector Machine (SVM) model. Finally, we have
analyzed the misclassified snippets and have discussed the challenges needed to
be resolved for better accuracy.
