Bidirectional transfer learning model for sentiment analysis of natural language

The contemporary unsupervised word representation methods have been successful in capturing semantic statistics on various Natural Language Processing tasks. However, these methods proved to be futile in addressing tasks like polysemy or homonymy, which prevail in such tasks. There has been a rise in the number of state-of-the-art transfer learning techniques bringing into play the language models pre-trained on large inclusive corpus. Motivated by these techniques, the present paper proposes an efficacious transfer learning based ensemble model. This model is inspired by ULMFit and presents results on challenging sentiment analysis tasks such as contextualization and regularization. We have empirically validated the efficiency of our proposed model by applying it to three conventional datasets for sentiment classification task. Our model accomplished the state-of-the-art outcomes remarkably when compared to acknowledged baselines in terms of classification accuracy.