Extracting key sentiment sentences from internet news via multiple source features

Extracting key sentences with sentiments from discourses plays an important role in sentiment analysis. Different from general discourses, Internet news has its own fashion of sentiment expression. In this paper, we attempt to extract key sentiment sentences from those Internet news articles. In this paper, we propose a method, called MSF, by using multiple sources features. In our method, for each sentence we first design four sources of features, including lexical sentiment, global position, word grammar indicator, and title similarity. Then, these features are linearly combined to obtain a score indicating the probability that the sentence is a key sentiment sentence. Experiments on a publicly available dataset show the effectiveness of our MSF method.