This paper describes the iLab-Edinburgh Sentiment Analysis system, winner of the Arabic Twitter Task 7 in SemEval-2016. The system employs a hybrid approach of supervised learning and rule-based methods to predict a sentiment intensity (SI) score for a given Arabic Twitter phrase. First, the supervised method uses an ensemble of trained linear regression models to produce an initial SI score fo...

Sentiment Analysis aims to get the underlying viewpoint of the text, which could be anything that holds a subjective opinion, such as an online review, Movie rating, Comments on Blog posts etc. This paper presents a novel approach that classify text in two-dimensional Emotional space, based on the sentiments of the author. The approach uses existing lexical resources to extract feature set, whi...

The paper proposes the Qualitative Modelling Toolbox (QuaMo Toolbox), a MATLAB compatible toolbox used to synthesise, analyse, supervise and control dynamic systems described by qualitative models. The nature of the qualitative models become obvious from the fact that they do not refer to the numerically precise signal but to symbolic signal values. Therefore the toolbox is useful in applicatio...

Classifying relations between pairs of medical concepts in clinical texts is a crucial task to acquire empirical evidence relevant to patient care. Due to limited labeled data and extremely unbalanced class distributions, medical relation classification systems struggle to achieve good performance on less common relation types, which capture valuable information that is important to identify. O...

Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis. For example, master, seasoned and familiar point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semisupervised technique that uses se...

We discuss the detection of two Gaussian clusters given a cloud of points. The optimal learning curve for this unsupervised learning scenario is determined with a replica calculation. A comparison with principal component analysis and supervised learning allows to understand the three diierent learning phases observed.

So far different studies have tackled the sentiment analysis in several domains such as restaurant and movie reviews. But, this problem has not been studied in scholarly book reviews which is different in terms of review style and size. In this paper, we propose to combine different features in order to be presented to a supervised classifiers which extract the opinion target expressions and de...

Current approaches to sentiment analysis assume that the sole discourse function of sentiment-bearing texts is expressivity. However, the persuasive discourse function also utilises expressive language. In this work, we present the results of training supervised classifiers on a new corpus of clinical texts that contain documents with an expressive discourse function, and we test the learned mo...

The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for single-view problems. In fact, in many real-world applications, examples are represented by multiple sets of features. In this paper, we extend t...

We argue that when objects are characterized by many attributes, clustering them on the basis of a relatively small random subset of these attributes can capture information on the unobserved attributes as well. Moreover, we show that under mild technical conditions, clustering the objects on the basis of such a random subset performs almost as well as clustering with the full attribute set. We...