Adaptive semi-supervised learning for cross-domain sentiment classification

Abstract

Methods, systems, and computer-readable storage media for receiving a source domain data set including a set of source document and source label pairs, each source label corresponding to a source domain and indicating a sentiment attributed to a respective source document, receiving a target domain data set including a set of target documents absent target labels, processing documents of the source and target domains using a feature encoder of a DAS platform, to map the documents of the source and target domains to a shared feature space through feature representations, the processing including minimizing a distance between the feature representations of the source domain, and feature representations of the target domain based on a set of loss functions, providing an ensemble prediction from the processing, and providing predicted labels based on the ensemble prediction, the predicted labels being used by the sentiment classifier to classify documents from the target domain.