Paper: Active Learning for Imbalanced Sentiment Classification

ACL ID D12-1013
Title Active Learning for Imbalanced Sentiment Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

Active learning is a promising way for sentiment classification to reduce the annotation cost. In this paper, we focus on the imbalanced class distribution scenario for sentiment classification, wherein the number of positive samples is quite different from that of negative samples. This scenario posits new challenges to active learning. To address these challenges, we propose a novel active learning approach, named co-selecting, by taking both the imbalanced class distribution issue and uncertainty into account. Specifically, our co-selecting approach employs two feature subspace classifiers to collectively select most informative minority-class samples for manual annotation by leveraging a certainty measurement and an uncertainty measurement, and in the meanwhile, aut...