In NTCIR-7 MOAT, we participated in four sub-tasks (opinion & holder detection, relevance judg-ment, and polarity classification) at two language sides: Japanese and English. In this paper, we fo-cused on the feature selection and polarity classifi-cation methodology in both languages. To detect opinion and classify the polarity, the features were selected based on a statistical χ-square tests over NTCIR-6 and MPQA corpora. We also compared several multi-label classification methods to clas-sify positive, negative, and neutral polarity. The evaluation results suggested that the coverage of the features in Japanese was acceptable for the opinion analysis in newspaper articles, but there was still a room for improvement in the coverage of the features in English. We also found the result of SVM voting approach was slightly better than the results of Multi-label classification approach.