Publicerad: 2013-05-17
ISBN: 978-91-7519-589-6
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
People tend to have various opinions about topics. In discussions; they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are; or help a company to analyze how well people like their new products. In this work; we develop an approach for recognizing agreement and disagreement. However; this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases; machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique; we achieve an accuracy of 72.85%. Besides; we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features.
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