Politics as text and talk:analytic approaches to political discourse

Human beings are political animals. They are also articulate mammals. How are these two aspects linked? This is a question that is only beginning to be explored. The present collection makes a contribution to the investigations into the use of language in those situations which, informally and intuitively, we call ‘political’. Such an approach is revealing not only for politics itself but also for the human language capacity. Each chapter outlines a particular method or analytic approach and illustrates its application to a contemporary political issue, institution or mode of political behaviour. As a whole, the collection aims to give a sample of current research in the field. It will interest those who are beginning to carry the research paradigm forward, as well as provide an introduction for newcomers, whether they come from neighbouring or remote disciplines or from none.

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.