New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

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.