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.

13C NMR of a single molecule magnet: analysis of pseudocontact shifts and residual dipolar couplings

Paramagnetic triple decker complexes of lanthanides are promising Single Molecule Magnets (SMMs), with many potential uses. Some of them show preferable relaxation behavior, which enables the recording of well resolved NMR spectra. These axially symmetric complexes are also strongly magnetically anisotropic, and this property can be described with the axial component of the magnetic susceptibility tensor, χa. For triple decker complexes with phthalocyanine based ligands, the Fermi˗contact contribution is small. Hence, together with the axial symmetry, the experimental chemical shifts in 1H and 13C NMR spectra can be modeled easily by considering pseudocontact and orbital shifts alone. This results in the determination of the χa value, which is also responsible for molecular alignment and consequently for the observation of residual dipolar couplings (RDCs). A detailed analysis of the experimental 1H-13C and 1H-1H couplings revealed that contributions from RDCs (positive and negative) and from dynamic frequency shifts (negative for all observed couplings) have to be considered. Whilst the pseudocontact shifts depend on the average positions of 1H and 13C nuclei relative to the lanthanide ions, the RDCs are related to the mobility of nuclei they correspond to. This phenomenon allows for the measurement of the internal mobility of the various groups in the SMMs.