Universally domain adaptive algorithm for sentiment classification using transfer learning approach

Huge amount of unstructured data is posted on the cloud from various sources for the purpose of feedback and reviews. These review needs require classification for many a reasons and sentiment classification is one of them. Sentiment classification of these reviews quite difficult as they are arriving from many sources. A robust classifier is needed to deal with different data distributions. Traditional supervised machine learning approaches not works well as they require retraining when domain is changed. Deep learning techniques perform well to handle these situations, but they are more data hungry and computationally expensive. Transfer learning is a feature in the cross-domain sentiment classification where features are transferred from one domain to another without any training. Moreover, transfer learning allows the domains, tasks, and distributions used in training and testing to be different. Therefor transfer learning mechanism is required to transfer the sentiment features across the domains. This paper presents a transfer learning approach using pretrained language model, ELMO which helps in transferring sentiment features across domains. This model has been tested on text reviews posted on twitter data set and compared with deep learning methods with and without pretraining process, also our model delivers promising results. This model permits flexibility to plug and play parameters with target models with easier domain adaptivity and transfer sentiment features. Also, model enables sentiment classifiers by using the transferred features from an already trained domain and reuse the sentiment features by saving the time and training cost.