Functional and molecular characterization of SR45, a plant-specific factor involved in sugar and stress signaling in Arabidopsis thaliana

Dissertation presented to obtain the Ph.D degree in Molecular Biology

Making connections: engineering and language issues in teaching english for specific purposes

Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Línguas, Literaturas e Culturas

Specific features of endothelial cells in primary tumors

RESUMO: As células endoteliais definem e delineiam todo o sistema vascular...Nesta tese procurámos explorar o papel que o ambiente tumoral exerce sobre as células endoteliais. ... Avaliamos também a capacidade anti-angiogénica de alguns derivados do estrogénio... Em suma os nossos resultados mostram a importância de um controlo rigoroso da regulação transcricional...

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.