Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
We combine active learning and self-training for cross-lingual sentiment classification. Density analysis of unlabelled data is used to select representative examples in active learning. We test our proposed model on three different target languages. Results show that incorporating density analysis can speed up learning process. Results show that combination of two approaches outperforms each individual method.
active learning self-training cross-lingual sentiment classification density measure