  Sentiment Analysis typically refers to using natural language processing,
text analysis and computational linguistics to extract affect and emotion based
information from text data. Our work explores how we can effectively use deep
neural networks in transfer learning and joint dual input learning settings to
effectively classify sentiments and detect hate speech in Hindi and Bengali
data. We start by training Word2Vec word embeddings for Hindi \textbf{HASOC
dataset} and Bengali hate speech and then train LSTM and subsequently, employ
parameter sharing based transfer learning to Bengali sentiment classifiers by
reusing and fine-tuning the trained weights of Hindi classifiers with both
classifier being used as baseline in our study. Finally, we use BiLSTM with
self attention in joint dual input learning setting where we train a single
neural network on Hindi and Bengali dataset simultaneously using their
respective embeddings.
