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Sentiment Analysis typically refers to using natural language processing,text analysis and computational linguistics to extract affect and emotion basedinformation from text data. Our work explores how we can effectively use deepneural networks in transfer learning and joint dual input learning settings toeffectively classify sentiments and detect hate speech in Hindi and Bengalidata. We start by training Word2Vec word embeddings for Hindi \textbf{HASOCdataset} and Bengali hate speech and then train LSTM and subsequently, employparameter sharing based transfer learning to Bengali sentiment classifiers byreusing and fine-tuning the trained weights of Hindi classifiers with bothclassifier being used as baseline in our study. Finally, we use BiLSTM withself attention in joint dual input learning setting where we train a singleneural network on Hindi and Bengali dataset simultaneously using theirrespective embeddings.

Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention