Efficient Stance Detection With Latent Feature

Social platforms, such as Twitter, are becoming more and more popular. However it is hard to identify the sentimental stance from those social media. In this paper, an approach is proposed to identify the stance of opinion. Digging out the latent factors of the given rough processed information is essential because it has the potential to reveal different aspects of the known information, which eventually contributes to the advancement of stance analysis. Generally, we take a very large number of articles from Chinese wikipedia as the corpus. The latent feature vectors are generated by word2vec. The HowNet sentiment dictionary (with positive and negative words) are applied to divide the items in the corpus into two parts. The two parts with sentiment polarity are used as the training set for SVM model. Experimentation on NLPCC 2016 Stance Detection dataset demonstrates that the proposed approach can outperform the baselines by about 10% in the term of precision.