An Unsupervised Snippet-Based Sentiment Classification Method for Chinese Unknown Phrases without Using Reference Word Pairs

This work presents an unsupervised snippet-based sentiment classification method for Chinese unknown sentiment phrases, which is also applicable to other languages theoretically. Unlike existing Semantic Orientation (SO) methods, our proposed method does not require any Reference Word Pairs (RWPs) for predicting the sentiments of phrases. The results of preliminary experiments show that our proposed method is highly effective and achieves over 80% accuracy and F-measures with relatively fewer queries. An experiment of opinion extraction using a public Chinese UGC corpus also shows promising results.