Hyperparameter tuning in convolutional neural networks for domain adaptation in sentiment classification (HTCNN-DASC)

Abstract

In-domain adaptation (DA), the knowledge trained in one domain, is used to test an unknown domain. Existing approaches use limited efforts on DA in sentiment classification (SC) using neural networks. The challenging task here is the dissimilarity in the semantic behavior across domains. In this paper, convolutional neural networks (CNNs) learn the knowledge of a particular domain using Doc2Vec feature representation which provides good performance for DA in SC for the target domain. Our empirical analysis with one-layer CNN exhibits significant change in the accuracy by tuning the hyperparameters involved with the CNN. This paper derives into a suitable CNN architecture accompanying hyperparameters which favor DA between different domains. Our empirical analysis with multi-domain dataset demonstrates that with suitable hyperparameters, CNN works well for DASC problems. The comparative study shows that CNN with Doc2Vec model provides a strong capability of learning large data representation semantically with other state-of-the-art methods for the DASC.