Abstract
The extraction of statements is an essential step in a Media Response Analysis (MRA), because statements in news represent the most important information for a customer of a MRA and can be used as the underlying data for Opinion Mining in newspaper articles. We propose a machine learning approach to tackle this problem. For each sentence, our method extracts different features which indicate the importance of a sentence for a MRA. Classified sentences are filtered through a density-based clustering, before selected sentences are combined to statements. In our evaluation, this technique achieved better results than comparison methods from Text Summarization and Opinion Mining on two real world datasets.
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Scholz, T., Conrad, S. (2013). Extraction of Statements in News for a Media Response Analysis. 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_1
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DOI: https://doi.org/10.1007/978-3-642-38824-8_1
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