Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement
Bernd Opitz, Cäcilia Zirn · DSpace repository (University of Tartu) · 2013
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.