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Nowadays, people do not only navigate the web, but they also contribute contents to the Internet. Among other things, they write their thoughts and opinions in review sites, forums, social networks, blogs and other websites. These opinions constitute a valuable resource for businesses, governments and consumers. In the last years, some researchers have proposed opinion extraction systems, mostly domain-independent ones, to automatically extract structured representations of opinions contained in those texts. In this work, we tackle this task in a domain-oriented approach, defining a set of domain-specific resources which capture valuable knowledge about how people express opinions on a given domain. These resources are automatically induced from a set of annotated documents. Some experiments were carried out on three different domains (user-generated reviews of headphones, hotels and cars), comparing our approach to other state-of-the-art, domain-independent techniques. The results confirm the importance of the domain in order to build accurate opinion extraction systems. Some experiments on the influence of the dataset size and an example of aggregation and visualization of the extracted opinions are also shown.

Trust and Reputation Systems constitute an essential part of many social networks due to the great expansion of these on-line communities in the past few years. As a consequence of this growth, some users try to disturb the normal atmosphere of these communities, or even to take advantage of them in order to obtain some kind of benefits. Therefore, the concept of trust is a key point in the performance of on-line systems such as on-line marketplaces, review aggregators, social news sites, and forums. In this work we propose a method to compute a ranking of the users in a social network, regarding their trustworthiness. The aim of our method is to prevent malicious users from illicitly gaining high reputation in the network by demoting them in the ranking of users. We propose a novel system intended to propagate both positive and negative opinions of the users through a network, in such way that the opinions from each user about others influence their global trust score. Our proposal has been evaluated in different challenging situations. The experiments include the generation of random graphs, the use of a real-world dataset extracted from a social news site, and a combination of both a real dataset and generation techniques, in order to test our proposals in different environments. The results show that our method performs well in every situations, showing the propagation of trust and distrust to be a reliable mechanism in a Trust and Reputation System.

This paper presents the ideas, experiments and specifications related to the Supervised TextRank (STR) technique, a word tagging method based on the TextRank algorithm. The main innovation of STR technique is the use of a graph-based ranking algorithm similar to PageRank in a supervised fashion, gathering the information needed to build the graph representations of the text from a tagged corpus. We also propose a flexible graph specification language that allows to easily experiment with multiple configurations for the topology of the graph and for the information associated to the nodes and the edges. We have carried experiments in the Part-Of-Speech task, a common tagging problem in Natural Language Processing. In our best result we have achieved a precision of 96.16%, at the same level of the best tagging tools.

In this paper we present InstanceRank, a ranking algorithm that reflects the relevance of the instances within a dataset. InstanceRank applies a similar solution to that used by PageRank, the web pages ranking algorithm in the Google search engine. We also present ISR, an instance selection technique that uses InstanceRank. This algorithm chooses the most representative instances from a learning database. Experiments show that ISR algorithm, with InstanceRank as ranking criteria, obtains similar results in accuracy to other instance reduction techniques, noticeably reducing the size of the instance set.

Feature-based opinion extraction is a task related to infor- mation extraction, which consists of extracting structured opinions on features of some object from reviews or other subjective textual sources. Over the last years, this prob-lem has been studied by some researchers, generally in an unsupervised, domain-independent manner. As opposed to that, in this work we propose a rede nition of the problem from a more practical point of view, and describe a domain- speci c, resource-based opinion extraction system. We fo-cus on the description and generation of those resources, and brie y report the extraction system architecture and a few initial experiments. The results suggest that domain-speci c knowledge is a valuable resource in order to build precise opinion extraction systems.

The incorporation of background knowledge in unsupervised algorithms has been shown to yield performance improvements in terms of model quality and execution speed. However, performance is dependent on the quantity and quality of the background knowledge being exploited. In this work, we study the issue of selecting Must-Link and Cannot-Link constraints for semi-supervised clustering. We propose "ConstraintSelector", an algorithm that takes as input a set of labeled data instances, from which constraints can be derived, ranks these instances on their usability and then derives constraints from the topranked instances only. Our experiments show that ConstraintSelector chooses, respectively reduces, the set of candidate constraints without compromising the quality of the derived model.