| Sumario: | In this thesis we study Opinion Mining and Sentiment Analysis and propose a ne-grained analysis of the opinions conveyed in texts. Concretely, the aim of this research is to gain an understanding on how to combine di erent types of evidence to e ectively determine on-topic opinions in texts. To meet this aim, we consider content-match evidence, obtained at document and passage level, as well as di erent structural aspects of the text. Current Opinion Mining technology is not mature yet. As a matter of fact, people often use regular search engines, which lack evolved opinion search ca- pabilities, to nd opinions about their interests. This means that the e ort of detecting what are the key relevant opinions relies on the user. The lack of widely accepted Opinion Mining technology is due to the limitations of cur- rent models, which are simplistic and perform poorly. In this thesis we study a speci c set of factors that are indicative of subjectivity and relevance and we try to understand how to e ectively combine them to detect opinionated docu- ments, to extract relevant opinions and to estimate their polarity. We propose innovative methods and models able to incorporate di erent types of evidence and it is our intention to contribute in di erent areas, including those related to i) search for opinionated documents, ii) detection of subjectivity at docu- ment and passage level, and iii) estimation of polarity. An important concern that guides this research is e ciency. Some types of evidence, such as discourse structure, have only been tested with small collections from narrow domains (e.g., movie reviews). We demonstrate here that evolved linguistic features { based on discourse analysis{ can potentially lead to a better understanding of how subjectivity ows in texts. And we show that this type of features can be e ciently injected into general-purpose opinion retrieval solutions that operate at large scale.
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