Semi-supervised collective extraction of opinion target and opinion word from online reviews based on active labeling

Abstract

Online reviews play important roles in many Web Applications like e-business and government intelligence, since such user-generated-contents (UGC) contain rich user opinion. Opinion target and opinion word are a pair of core objects for user opinion expression in reviews. Extracting these two objects from reviews is crucial for the tasks of opinion mining. However, traditional extraction methods have various limitations such as ignoring the opinion relationship, the restriction of word span, the error propagation caused by iterative expansion, which would reduce the extraction performance. For the above deficiencies, we propose a supervised method based on the constrained word alignment model to extract opinion target and opinion word collectively at first. To tackle the time-consuming and error-prone problem of manual annotation encountered by the supervised method, we further devise a semi-supervised extraction method based on active learning. In this method, we design the sample uncertainty-based sampling strategy and the feature evidence-based one to choose the most informative samples for labeling manually. At last, a series of experiments on a real-world dataset show that our approaches outperform several state-of-the-art baselines significantly.