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.
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Scholz, T., Conrad, S. (2013). Linguistic Sentiment Features for Newspaper Opinion Mining. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_24
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DOI: https://doi.org/10.1007/978-3-642-38824-8_24
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