  Sentiment analysis (SA) using code-mixed data from social media has several
applications in opinion mining ranging from customer satisfaction to social
campaign analysis in multilingual societies. Advances in this area are impeded
by the lack of a suitable annotated dataset. We introduce a Hindi-English
(Hi-En) code-mixed dataset for sentiment analysis and perform empirical
analysis comparing the suitability and performance of various state-of-the-art
SA methods in social media.
  In this paper, we introduce learning sub-word level representations in LSTM
(Subword-LSTM) architecture instead of character-level or word-level
representations. This linguistic prior in our architecture enables us to learn
the information about sentiment value of important morphemes. This also seems
to work well in highly noisy text containing misspellings as shown in our
experiments which is demonstrated in morpheme-level feature maps learned by our
model. Also, we hypothesize that encoding this linguistic prior in the
Subword-LSTM architecture leads to the superior performance. Our system attains
accuracy 4-5% greater than traditional approaches on our dataset, and also
outperforms the available system for sentiment analysis in Hi-En code-mixed
text by 18%.
