  Code mixing is a common phenomena in multilingual societies where people
switch from one language to another for various reasons. Recent advances in
public communication over different social media sites have led to an increase
in the frequency of code-mixed usage in written language. In this paper, we
present the Generative Morphemes with Attention (GenMA) Model sentiment
analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to
predict the sentiments of the given English-Hindi code-mixed tweets without
using word-level language tags instead inferring this automatically using a
morphological model. The system is based on a novel deep neural network (DNN)
architecture, which has outperformed the baseline F1-score on the test data-set
as well as the validation data-set. Our results can be found under the user
name "koustava" on the "Sentimix Hindi English" page
