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.
Additional information:
Online ressource. - NEALT proceedings series ; 16. - http://emmtee.net/oe/nodalida13/conference/28.pdf
Dieser Eintrag ist Teil der Universitätsbibliographie.
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