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

Abstract

Sentiment analysis typically refers to using natural language processing, text analysis, and computational linguistics to extract effect 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 HASOC data set and Bengali hate speech (1) and then train long short-term memory 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 classifiers being used as the baseline in our study. Finally, we use BiLSTM with self-attention in a joint dual-input learning setting where we train a single neural network on the Hindi and Bengali data sets simultaneously using their respective embeddings.