Traditional lexicon-based approaches for sentiment analysis are usually not able to model negation, as they do not provide proper techniques to identify the right negation window. In this work we address the problem of the automatic determination of the scope of negation and we present a negation handling algorithm based on dependency-based parse trees. The proposal is based on the use of grammatical relations among words to model a sentence, and hence to determine words that are affected by negation. The proposed algorithm has been coupled with a semantic disambiguation technique to identify the sentiment of a sentence. Experiments on different datasets have proven that our proposal improves the accuracy of the sentiment analysis. The proposed algorithm has been implemented as part of a Social Information Discovery system, which allows for an integrated near-real-time analysis of discussions from multiple social networks.

The class-imbalance problem is the problem of learning a classification rule from data that are skewed in favor of one class. On these datasets traditional learning techniques tend to overlook the less numerous class, at the advantage of the majority class. However, the minority class is often the most interesting one for the task at hand. For this reason, the class-imbalance problem has received increasing attention in the last few years. In the present paper we point the attention of the reader to a learning algorithm for the minimization of the average misclassification risk. In contrast to some popular class-imbalance learning methods, this method has its roots in statistical decision theory. A particular interesting characteristic is that when class distributions are unknown, the method can work by resorting to stochastic gradient algorithm. We study the behavior of this algorithm on imbalanced datasets, demonstrating that this principled approach allows to obtain better classification performances compared to the principal methods proposed in the literature.