There is high demand for computational tools that can automatically label tweets (Twitter messages) as having positive or negative sentiment, but great effort and expense would be required to build a large enough hand-labeled training corpus on which to apply standard machine learning techniques. Going beyond current keyword-based heuristic techniques, this paper uses emoticons (e.g. ':)' and ':(') to collect a large training set with noisy labels using little human intervention and trains a Maximum Entropy classifier on that training set. Results on two hand-labeled test corpora are compared to various baselines and a keyword-based heuristic approach, with the machine learned classifier significantly outperforming both. / text

In the light of the ongoing loss of biodiversity at the global scale, monitoring grasslands is nowadays of utmost importance considering their functional relevance in terms of the ecosystem services that they provide. Here, guidelines of the European Union like the Fauna-Flora-Habitat Directive and the European Agricultural fund for Rural Development with its HNV indicators are crucial. Indeed, they form the legal framework for nature conservation and define grasslands as one of their conservation targets, whose status needs to be assessed and reported by all member states on a regular basis. In the light of these reporting requirements, the need for a harmonised and thorough grassland monitoring is highly demanding since most member states are still currently adopting intensive field surveys or photointerpretation with differing levels of detail for mapping habitat distribution. To this purpose, a cost-effective solution is offered by Earth Observation data for which specific grassland monitoring methodologies shall be then implemented which are capable of processing multitemporal acquisitions collected throughout the entire growing season. Although optical data are most suited for characterising vegetation in terms of spectral information content, they are actually subject to weather conditions (especially cloud coverage), which hinder the possibility of collecting enough information over the full phenological cycle. Furthermore, so far only few studies started employing high and very high resolution optical time series for grassland habitat monitoring since they have become available e.g., from the RapidEye satellites, only in the recent past. To overcome this limitation, SAR systems can be employed which provide imagery independent from weather or daytime conditions, hence enabling vegetation analysis by means of complete time series. Compared to optical data, radar imagery is less affected by the physical-chemical characteristics of the surface, but rather it is sensitive to structural features like geometry and roughness. However, in this context presently only very few techniques have been implemented, which are anyhow not suitable to be employed in an operational framework. Furthermore, to address the classification task, supervised approaches (which require in situ information for all the land-cover classes present in the study area) represent the most accurate methodological solution; nevertheless, collecting an exhaustive ground truth is generally expensive both in terms of time and economic costs and is not even feasible when the test site is remote. However, in many applications the end-users are generally only interested in very few specific targeted land-cover classes which, for instance, have high ecological value or are associated with support actions, subsidies or benefits from national or international institutions. The categorisation of specific grasslands and habitat types as those addressed in this thesis falls within such category of problems, which is defined in the literature as targeted land-cover classification. In this framework, a robust and effective targeted classification system for the automatic identification of grassland types by means of multi-temporal and multi-polarised SAR data has been developed within this thesis. In particular, the proposed system is composed of three main blocks: the preprocessing of the SAR image time series including the Kennaugh decomposition, the feature extraction including multi-temporal filtering and texture analysis, and the hierarchical targeted classification, which consist of two phases where first a one-class classifier is employed to outline the merger of all the grassland types of interest considered as a single information class and then a multi-class classifier is applied for discriminating the specific targeted classes within the areas identified as positive by the one-class classifier. To evaluate the capabilities of the proposed methodology, several experimental trials have been carried out over two test sites located in Southern Bavaria (Germany) and Mecklenburg Western-Pomerania (Germany) for which six diverse datasets have been derived from multitemporal series of dualpol TerraSAR-X as well as dual-/quadpol Radarsat-2 images. Four among the Natura 2000 habitat types of the Fauna-Flora-Habitat Directive as well all High Nature Value grassland types have been considered as targeted classes for this study. Overall, the proposed system proved to be robust and confirmed the effectiveness of employing multitemporal and multi-polarisation VHR SAR data for discriminating habitat types and High Nature Value grassland types, exhibiting high potential for future employment even at larger scales. In particular, it could be demonstrated that the proposed hierarchical targeted classification approach outperforms the available state-of-the-art methods and has a clear advantage with respect to the standard approaches in terms of robustness, reliability and transferability.