Sentiment Classification in Resource-Scarce Languages by using Label Propagation

With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, which are difficult to obtain in resource-scarce languages. To overcome this problem, we investigate the usefulness of label propagation, which is a graph-based semi-supervised learning method. Extensive experimental evaluation on three real datasets demonstrated that label propagation performs more stable than support vector machines (SVMs) and transductive support vector machines (TSVMs) in a document-level sentiment classification task for resource-scarce languages (Chinese in our case).