CLOpinionMiner: Opinion Target Extraction in a Cross-Language Scenario Opinion target extraction is a subtask of opinion mining which is very useful in many applications. The problem has usually been solved by training a sequence labeler on manually labeled data. However, the labeled training datasets are imbalanced in different languages, and the lack of labeled corpus in a language limits the research progress on opinion target extraction in this language. In order to address the above problem, we propose a novel system called CLOpinionMiner which investigates leveraging the rich labeled data in a source language for opinion target extraction in a different target language. In this study, we focus on English-to-Chinese cross-language opinion target extraction. Based on the English dataset, our method produces two Chinese training datasets with different features. Two labeling models for Chinese opinion target extraction are trained based on Conditional Random Fields (CRF). After that, we use a monolingual co-training algorithm to improve the performance of both models by leveraging the enormous unlabeled Chinese review texts on the web. Experimental results show the effectiveness of our proposed approach.