Salud María Jiménez-Zafra

TL;DR: This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015, which attracted 245 submissions from 29 teams and provided 19 training and 20 testing datasets.

TL;DR: This study examines how people express their opinion in medical forums to determine the best way to tackle sentiment analysis in this domain and shows that drug reviews are more difficult to classify than those about physicians.

TL;DR: An unsupervised approach for aspect-based sentiment analysis is described, which aims to identify the aspects of given target entities and the sentiment expressed for each aspect and concludes that the combination of several classifiers improves the classification significantly.

TL;DR: The methodology applied in the annotation of the corpus including the tagset, the linguistic criteria and the inter-annotator agreement tests are described, and a complete typology of negation patterns in Spanish is included.

TL;DR: In this article , the authors examine which features are most effective in identifying hate-speech in Spanish and how these features can be combined to develop more accurate systems, and characterize the language present in each type of hatespeech by means of explainable linguistic features and compare their results with state of the art approaches.