关键词: 立场检测, 情感分析, 文本特征表示, 微博, 文本分类

Abstract: Stance detection aims to automatically determine whether the author of a text is in favor of the given target, against the given target, or neither. This paper presents a stance detection system based on multiple text feature representations. Firstly, five different feature representations are explored including statistic-based features（BoW, synonym-based BoW, sVariance） and deep text features（word vectors and character vectors）. Support Vector Machine（SVM）, Random Forest and Gradient Boosting Decision Tree（GBDT） are applied as classifiers. Finally, late fusion is conducted to combine different feature representations. Experiment results show that the proposed feature representations can achieve significant improvement over traditional BoW feature. Moreover, statistic-based features and deep features provide complementary information for stance detection, which leads to the wining system in the Chinese Microblog Stance Detection Evaluation by Natural Language Processing and Chinese Computing（NLPCC 2016）.

Key words: stance detection, sentiment analysis, text feature representations, Chinese Microblogs, text classification