Linguistic sentiment features for newspaper opinion mining

Abstract

The sentiment in news articles is not created only through single words, also linguistic factors, which are invoked by different contexts, influence the opinion-bearing words. In this paper, we apply various commonly used approaches for sentiment analysis and expand research by analysing semantic features and their influence to the sentiment. We use a machine learning approach to learn from these features/influences and to classify the resulting sentiment. The evaluation is performed on two datasets containing over 4,000 German news articles and illustrates that this technique can increase the performance. © 2013 Springer-Verlag Berlin Heidelberg.