Salud María Jiménez Zafra

• University of Jaén

This paper presents the main sources of disagreement found during the annotation of the Spanish SFU Review Corpus with negation (SFU Review SP-NEG). Negation detection is a challenge in most of the task related to NLP, so the availability of corpora annotated with this phenomenon is essential in order to advance in tasks related to this area. A tho...

In this paper we present the criteria applied for the annotation of the SFU ReviewSP-NEG corpus and the corresponding linguistic typology. This typology has the advantage that it is easy to express in terms of a tagset for corpus annotation: the types are clearly defined, which avoid the ambiguity in the annotation process, and they present a wide...

Every day more companies are interested in users’ opinions about their products or services. Also, every day there are more users that search for reviews on the web before purchasing a product. These users and companies are not satisfied with knowing the overall sentiment of a product, they want a finer knowledge of users’ opinions. Owing to this f...

Arabic is a language with rich morphology and few resources. Arabic is therefore recognized as one of the most challenging languages for machine translation. The study of translation into Arabic has received significantly less attention than that of European languages. Consequently, further research into Arabic machine translation quality needs mor...

Sentiment Analysis, also known as opinion mining, is the area of Natural Language Processing that aims to extract human perceptions, thoughts, and beliefs from unstructured textual content. It has become a useful, attractive, and challenging research area concerning the emergence and rise of social media and the mass volume of individuals’ reviews,...

In recent years, systems have been developed to monitor online content and remove abusive, offensive or hateful content. Comments in online social media have been analyzed to find and stop the spread of negativity using methods such as hate speech detection, identification of offensive language or detection of abusive language. We define hope speec...

Sentiment analysis is one of the biggest challenges for mining textual online content generated by users. This paper concentrates on reviews and feedback from customers, which are relevant content types representing opinions. The aim of this work is to identify the semantic orientation of each sentence (e.g. positive or negative). Traditional metho...

Este trabajo analiza el discurso de odio en los mensajes publicados en Twitter desde el 17 al 25 de mayo de 2021 durante la crisis producida por la entrada de miles de inmigrantes en la frontera del Tarajal en Ceuta. El objetivo de la investigación es realizar una clasificación temática de los mensajes que incluyen discurso de odio. Para ello, se h...

Artificial Intelligence has gained a lot of popularity in recent years thanks to the advent of, mainly, Deep Learning techniques [...]

The rise of social networks has allowed misogynistic, xenophobic, and homophobic people to spread their hate-speech to intimidate individuals or groups because of their gender, ethnicity or sexual orientation. The consequences of hate-speech are devastating, causing severe depression and even leading people to commit suicide. Hate-speech identifica...

Virality on Twitter is catching the attention of researchers, trying to identify factors which increase or decrease the probability of retweeting. We study how terms expressing sentiments affect retweeting frequencies by means of a regression model on the number of retweets, which is specially accurate to deal with virality. We focus on the Spanish...

The Task on Semantic Analysis at SEPLN (TASS task within IberLEF 2020 workshop) took place on September 22, reaching its ninth edition. Due to the COVID-19 pandemic, the number of participants is lower compared to past campaigns. Also, the organizers decided to held it remotely. In this edition, the classical polarity classification subtask was, ag...

Accurate negation identification is one of the most important tasks in the context of sentiment analysis. In order to correctly interpret the sentiment value of a particular expression, we need to identify whether it is in the scope of negation. While much of the work on negation detection has focused on English, we have seen recent developments th...

Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet...

In this paper, we present SFU ReviewSP-NEG, the first Spanish corpus annotated with negation with a wide coverage freely available. We describe the methodology applied in the annotation of the corpus including the tagset, the linguistic criteria and the inter-annotator agreement tests.We also include a complete typology of negation patterns in Span...

Polarity classification is a well-known Sentiment Analysis task. However, most research has been oriented towards developing supervised or unsupervised systems without paying much attention to certain linguistic phenomena such as negation. In this paper we focus on this specific issue in order to demonstrate that dealing with negation can improve t...

Sentiment Analysis is a well-known task of Natural Language Processing that has been studied in different domains such as movies, phones or hotels. However, other areas like medical domain remain yet unexplored. In this paper we study different polarity classification techniques applied on health domain. We present a corpus of patient reviews compo...

This document presents a description of the Entity Linking system known as REDES, which was involved into the track Entity Discovery and Linking (EDL) of the challenge TAC Knowledge Base Population (KBP) 2016. The system developed is result of the collaboration among different research projects, in particular SAM, from which specific modules were r...

Every day more users are interested in the opinion that other patients have about a physician or about health topics in general. According to a study in 2015, 62% of Spanish people access the Internet in order to be informed about topics related to health. This paper is focused on Spanish Sentiment Analysis in the medical domain. Although Sentiment...

Resumen: Desde que la web 2.0 es el mayor contenedor de opiniones en todos los idiomas sobre distintos temas o asuntos, el estudio del Análisis de Sentimientos ha crecido exponencialmente. En este trabajo nos centramos en la clasificación de polaridad de opiniones en español y se presenta un nuevo recurso léxico adaptado al dominio turístico (eSOLH...

Currently, reputation and sentiment analysis are trendy tasks. However, the interest on these two tasks is growing by the need of knowing the polarity of the opinions published on the Internet. TASS is a workshop whose goal is to boost the research on sentiment analysis in Spanish. Hereinafter the third issue of TASS is described, in which four tas...

This article presents the participation of the SINAI research group in the task Sentiment Analysis in Twitter of the SemEval Workshop. Our proposal consists of a voting system of three polarity classifiers which follow a lexicon-based approach.

This paper describes the participation of the SINAI research group in Task 4 of the 2014 edition of the International Workshop SemEval. This task is concerned with Aspect Based Sentiment Analysis and its goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.

En este trabajo se presenta una primera aproximación a dos de los frentes abiertos en el análisis de opiniones en los que nos encontramos trabajando actualmente: el tratamiento de la negación en español y el análisis a nivel de aspecto en inglés. Para abordar el fenómeno de la negación se han definido una serie de reglas que permiten determinar el...