In Your Face! Investigating Facework, Criticism, and Humour with Corpora in Electoral Debates

In political debates, the media[tisation] can determine the use of language with the aim to increase their spectacularisation and polarisation, possibly by means of criticism and humour, respectively. These linguistic strategies are often used in order to shape what was defined by Goffman as one’s face. Politicians, in particular, can recur to facework in a double sense: shaping their own face positively and/or that of their opponents negatively. Starting from the sociologic theory of face by Goffman and Levinson, with the help of corpus analysis tools, this research investigated the ways in which various forms of criticism and forms of humour were conducted in 3 electoral debates on a national scale (Germany, Ireland, and New Zealand) and 1 debate for the municipal election in Rome. The transcripts were revised after automatic transcriptions were extracted or found online, of which the audio-visual content is available on the Internet. The CADS research aimed to investigate the role that criticism and humour played within each participant’s discourse, and to identify differences and similarities among the strategies used by political leaders and moderators in different countries, and in different cultural, political, and media contexts.

Classificazione di oggetti in immagini attraverso il modello Bag of Visual Words

Generic object recognition is an important function of the human visual system and everybody finds it highly useful in their everyday life. For an artificial vision system it is a really hard, complex and challenging task because instances of the same object category can generate very different images, depending of different variables such as illumination conditions, the pose of an object, the viewpoint of the camera, partial occlusions, and unrelated background clutter. The purpose of this thesis is to develop a system that is able to classify objects in 2D images based on the context, and identify to which category the object belongs to. Given an image, the system can classify it and decide the correct categorie of the object. Furthermore the objective of this thesis is also to test the performance and the precision of different supervised Machine Learning algorithms in this specific task of object image categorization. Through different experiments the implemented application reveals good categorization performances despite the difficulty of the problem. However this project is open to future improvement; it is possible to implement new algorithms that has not been invented yet or using other techniques to extract features to make the system more reliable. This application can be installed inside an embedded system and after trained (performed outside the system), so it can become able to classify objects in a real-time. The information given from a 3D stereocamera, developed inside the department of Computer Engineering of the University of Bologna, can be used to improve the accuracy of the classification task. The idea is to segment a single object in a scene using the depth given from a stereocamera and in this way make the classification more accurate.