The implementation of an Enterprise Geographical Information System to support Cadastre and Expropriation activities.

The processes of mobilization of land for infrastructures of public and private domain are developed according to proper legal frameworks and systematically confronted with the impoverished national situation as regards the cadastral identification and regularization, which leads to big inefficiencies, sometimes with very negative impact to the overall effectiveness. This project report describes Ferbritas Cadastre Information System (FBSIC) project and tools, which in conjunction with other applications, allow managing the entire life-cycle of Land Acquisition and Cadastre, including support to field activities with the integration of information collected in the field, the development of multi-criteria analysis information, monitoring all information in the exploration stage, and the automated generation of outputs. The benefits are evident at the level of operational efficiency, including tools that enable process integration and standardization of procedures, facilitate analysis and quality control and maximize performance in the acquisition, maintenance and management of registration information and expropriation (expropriation projects). Therefore, the implemented system achieves levels of robustness, comprehensiveness, openness, scalability and reliability suitable for a structural platform. The resultant solution, FBSIC, is a fit-for-purpose cadastre information system rooted in the field of railway infrastructures. FBSIC integrating nature of allows: to accomplish present needs and scale to meet future services; to collect, maintain, manage and share all information in one common platform, and transform it into knowledge; to relate with other platforms; to increase accuracy and productivity of business processes related with land property management.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.